Articles | Volume 23, issue 10
https://doi.org/10.5194/hess-23-4153-2019
© Author(s) 2019. 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-23-4153-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Oct 2019
Research article |
| 09 Oct 2019
| 09 Oct 2019
Statistical characteristics of raindrop size distribution during rainy seasons in the Beijing urban area and implications for radar rainfall estimation
Yu Ma
State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Guangheng Ni
State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Chandrasekar V. Chandra
Colorado State University, Fort Collins, CO 80523, USA
Fuqiang Tian
State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Colorado State University, Fort Collins, CO 80523, USA
NOAA/Earth System Research Laboratory, Boulder, CO 80305, USA
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Hydrological models are increasingly combining scientific knowledge with AI to improve predictions, but it remains unclear whether these improvements also affect the reliability of model results. We found that AI improved prediction accuracy but also increased uncertainty in the processes it directly represented, while internal water storage retained and accumulated this uncertainty. These findings provide guidance for developing more reliable models for understanding water systems.
Zhen Cui and Fuqiang Tian
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Wenyue Zou, Ruidong Li, Daniel B. Wright, Jovan Blagojevic, Peter Molnar, Mohammad A. Hussain, Yue Zhu, Yongkun Li, Guangheng Ni, and Nadav Peleg
Hydrol. Earth Syst. Sci., 30, 5097–5116, https://doi.org/10.5194/hess-30-5097-2026, https://doi.org/10.5194/hess-30-5097-2026, 2026
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We present a framework using observed rainfall and temperature to generate realistic storms and simulate street-scale flooding for present and future climates. It integrates temperature-based rainfall scaling, storm-frequency estimation, and urban flood modeling, demonstrated in Beijing to assess changes in regional storm and flood depth, timing, and flow velocity. The workflow is data-light, physically grounded, and transferable worldwide.
Zixi Li, Yan Li, Rui Tong, Peizhe Cheng, Fuqiang Tian, and Yao Zhuang
Hydrol. Earth Syst. Sci., 30, 4909–4925, https://doi.org/10.5194/hess-30-4909-2026, https://doi.org/10.5194/hess-30-4909-2026, 2026
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Satellite soil moisture is too coarse and ground measurements are too sparse to describe field conditions. Drone microwave sensing helps fill this gap, but mixed signals from vegetation and surface variability reduce accuracy. We combine drone microwave, optical, and thermal data in a Bayesian framework to improve soil moisture estimates and quantify uncertainty. Field tests in China show higher accuracy, lower bias, and highlight small-scale heterogeneity as a key source of uncertainty.
Zixi Li and Fuqiang Tian
Hydrol. Earth Syst. Sci., 30, 3203–3219, https://doi.org/10.5194/hess-30-3203-2026, https://doi.org/10.5194/hess-30-3203-2026, 2026
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Forests can change the kinetic energy of rain below them. We built a new model that breaks down the canopy into layers, and tracks two types of raindrop: direct splashes and water dripping from leaves. The model was validated through nine rainfall events. The canopy doesn't always reduce the rain's force, and sometimes it increases it, depending on the specific structure of the leaves and branches.
Hongling Zhao, Zilong Zhao, Khosro Morovati, Dongsheng Zhang, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 30, 2433–2453, https://doi.org/10.5194/hess-30-2433-2026, https://doi.org/10.5194/hess-30-2433-2026, 2026
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Keer Zhang, Zilong Zhao, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 30, 671–691, https://doi.org/10.5194/hess-30-671-2026, https://doi.org/10.5194/hess-30-671-2026, 2026
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Brian C. Filipiak, David B. Wolff, Aaron Spaulding, Ali Tokay, Charles N. Helms, Adrian M. Loftus, Alexey V. Chibisov, Carl Schirtzinger, Mick J. Boulanger, Charanjit S. Pabla, Larry Bliven, EunYeol Kim, Francesc Junyent, V. Chandrasekar, Hein Thant, Branislav M. Notaros, Gustavo Britto Hupsel de Azevedo, and Diego Cerrai
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Yi Nan, Fuqiang Tian, and Mahmut Tudaji
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Diego Avesani, Yi Nan, and Fuqiang Tian
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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.
Ruidong Li, Jiapei Liu, Ting Sun, Jian Shao, Fuqiang Tian, and Guangheng Ni
Hydrol. Earth Syst. Sci., 29, 5677–5694, https://doi.org/10.5194/hess-29-5677-2025, https://doi.org/10.5194/hess-29-5677-2025, 2025
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Marc Schneebeli, Andreas Leuenberger, Philipp J. Schmid, Jacopo Grazioli, Heather Corden, Alexis Berne, Patrick Kennedy, Jim George, Francesc Junyent, and V. Chandrasekar
Atmos. Meas. Tech., 18, 5157–5176, https://doi.org/10.5194/amt-18-5157-2025, https://doi.org/10.5194/amt-18-5157-2025, 2025
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Hydrol. Earth Syst. Sci., 29, 2633–2654, https://doi.org/10.5194/hess-29-2633-2025, https://doi.org/10.5194/hess-29-2633-2025, 2025
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Mahmut Tudaji, Yi Nan, and Fuqiang Tian
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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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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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Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
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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.
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.
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.
Yabin Gou, Haonan Chen, Hong Zhu, and Lulin Xue
Atmos. Chem. Phys., 23, 2439–2463, https://doi.org/10.5194/acp-23-2439-2023, https://doi.org/10.5194/acp-23-2439-2023, 2023
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This article investigates the complex precipitation microphysics associated with super typhoon Lekima using a host of in situ and remote sensing observations, including rain gauge and disdrometer data, as well as polarimetric radar observations. The impacts of precipitation microphysics on multi-source data consistency and radar precipitation estimation are quantified. It is concluded that the dynamical precipitation microphysical processes must be considered in radar precipitation estimation.
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.
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.
Nicholas J. Kedzuf, J. Christine Chiu, V. Chandrasekar, Sounak Biswas, Shashank S. Joshil, Yinghui Lu, Peter Jan van Leeuwen, Christopher Westbrook, Yann Blanchard, and Sebastian O'Shea
Atmos. Meas. Tech., 14, 6885–6904, https://doi.org/10.5194/amt-14-6885-2021, https://doi.org/10.5194/amt-14-6885-2021, 2021
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Ice clouds play a key role in our climate system due to their strong controls on precipitation and the radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open up many research opportunities.
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Short summary
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.
Raindrop size distribution (DSD) information is fundamental in understanding the precipitation...
