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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon
Chao Wang, Shijie Jiang, Yi Zheng, et al.
Water Resources Research (2024) Vol. 60, Iss. 4
Open Access | Times Cited: 20

Showing 20 citing articles:

Artificial intelligence for geoscience: Progress, challenges and perspectives
Tianjie Zhao, Sheng Wang, Chaojun Ouyang, et al.
The Innovation (2024) Vol. 5, Iss. 5, pp. 100691-100691
Open Access | Times Cited: 59
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, et al.
Earth s Future (2024) Vol. 12, Iss. 7
Open Access | Times Cited: 31
Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets
F. M. Hasan, Paul Medley, Jason Drake, et al.
Water (2024) Vol. 16, Iss. 13, pp. 1904-1904
Open Access | Times Cited: 11
Soil and Water Assessment Tool (SWAT)-Informed Deep Learning for Streamflow Forecasting with Remote Sensing and In Situ Precipitation and Discharge Observations
Chunlin Huang, Ying Zhang, Jinliang Hou
Remote Sensing (2024) Vol. 16, Iss. 21, pp. 3999-3999
Open Access | Times Cited: 5
Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models
L. Zhong, Huimin Lei, Zhiyuan Li, et al.
Journal of Hydrology (2024) Vol. 645, pp. 132165-132165
Closed Access | Times Cited: 4
A surrogate model for the variable infiltration capacity model using physics-informed machine learning
Haiting Gu, Xiao Liang, Li Liu, et al.
Journal of Water and Climate Change (2025)
Open Access
Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach
Liangkun Deng, Xiang Zhang, Louise Slater
Journal of Hydrology (2025) Vol. 655, pp. 132895-132895
Closed Access
On the future of hydroecological models of everywhere
Keith Beven
Environmental Modelling & Software (2025), pp. 106431-106431
Closed Access
Physics-encoded deep learning for integrated modeling of watershed hydrology and reservoir operations
Bofu Yu, Yi Zheng, Shaokun He, et al.
Journal of Hydrology (2025), pp. 133052-133052
Closed Access
High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning
Yalan Song, Tadd Bindas, Chaopeng Shen, et al.
Water Resources Research (2025) Vol. 61, Iss. 4
Open Access
Improving differentiable hydrologic modeling with interpretable forcing fusion
Kamlesh Sawadekar, Yalan Song, Ming Pan, et al.
Journal of Hydrology (2025), pp. 133320-133320
Closed Access
A Multi‐Resolution Deep‐Learning Surrogate Framework for Global Hydrological Models
Bram Droppers, Marc F. P. Bierkens, Niko Wanders
Water Resources Research (2025) Vol. 61, Iss. 4
Open Access
Physics-informed machine learning in geotechnical engineering: a direction paper
Biao Yuan, Chung Siung Choo, Lit Yen Yeo, et al.
Geomechanics and Geoengineering (2025), pp. 1-32
Open Access
Detection and attribution of eco-hydrological alteration based on deep learning-driven gap-filled runoff in a large-scale catchment
Zhibao Dong, Xuan Ji, Kai Ma
Journal of Hydrology Regional Studies (2025) Vol. 58, pp. 102228-102228
Open Access
A Novel Hybrid Deep Learning Framework for Evaluating Field Evapotranspiration Considering the Impact of Soil Salinity
Yao Rong, Weishu Wang, Peijin Wu, et al.
Water Resources Research (2024) Vol. 60, Iss. 9
Open Access | Times Cited: 1
Impacts of land use changes on water conservation in the Songhuajiang River basin in Northeast China using the SWAT model
Beibei Ding, Yuqian Li, Gary W. Marek, et al.
Agricultural Water Management (2024) Vol. 306, pp. 109185-109185
Open Access | Times Cited: 1
A differentiable, physics-based hydrological model and its evaluation for data-limited basins
Wenyu Ouyang, Lei Ye, Yikai CHAI, et al.
Journal of Hydrology (2024), pp. 132471-132471
Closed Access | Times Cited: 1
Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning
Miao He, S. S. Jiang, Liliang Ren, et al.
Journal of Hydrology (2024) Vol. 649, pp. 132440-132440
Closed Access | Times Cited: 1
Editorial to the Special Issue “Recent Advances in Hydrological Modeling”
Minxue He, Seong Jin Noh, Haksu Lee
Hydrology (2024) Vol. 11, Iss. 7, pp. 108-108
Open Access
Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework
Haiting Gu, Yutai Ke, Zhixu Bai, et al.
Water (2024) Vol. 16, Iss. 22, pp. 3335-3335
Open Access
Recent advances in groundwater pollution research using machine learning from 2000 to 2023: a bibliometric analysis
Xuan Li, Guohua Liang, Bin He, et al.
Environmental Research (2024) Vol. 267, pp. 120683-120683
Closed Access
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