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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:

Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
L. Zhong, Huimin Lei, Bing Gao
Water Resources Research (2023) Vol. 59, Iss. 6
Closed Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration
Sungwook Wi, Scott Steinschneider
Hydrology and earth system sciences (2024) Vol. 28, Iss. 3, pp. 479-503
Open Access | Times Cited: 13
Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review
Bisrat Ayalew Yifru, Kyoung Jae Lim, Seoro Lee
Sustainability (2024) Vol. 16, Iss. 4, pp. 1376-1376
Open Access | Times Cited: 9
Coupling machine learning and physical modelling for predicting runoff at catchment scale
Sergio Zubelzu, Abdulmomen Ghalkha, Chaouki Ben Issaid, et al.
Journal of Environmental Management (2024) Vol. 354, pp. 120404-120404
Open Access | Times Cited: 6
Soil freeze/thaw dynamics strongly influences runoff regime in a Tibetan permafrost watershed: Insights from a process-based model
Huiru Jiang, Yonghong Yi, Kun Yang, et al.
CATENA (2024) Vol. 243, pp. 108182-108182
Closed Access | Times Cited: 5
Integrated forecasting of monthly runoff considering the combined effects of teleconnection factors
Jianbo Chang, Baowei Yan, Mingbo Sun, et al.
Journal of Hydrology Regional Studies (2025) Vol. 58, pp. 102206-102206
Closed Access
Nutrientscape ecology: a whole-system framework to support the understanding and management of coastal nutrient connectivity
Pirta Palola, Simon J. Pittman, Antoine Collin, et al.
Landscape Ecology (2025) Vol. 40, Iss. 3
Open Access
Integrating machine learning with process-based glacio-hydrological model for improving the performance of runoff simulation in cold regions
Babak Mohammadi, Hongkai Gao, Petter Pilesjö, et al.
Journal of Hydrology (2025), pp. 132963-132963
Open Access
Assessing the Runoff Response to Vegetation Cover and Climate Change in a Typical Forested Headwater Watershed
Ge Zhang, Junjie Xue, Wenting Liu, et al.
Water Resources Management (2025)
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
Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions
L. Zhong, Huimin Lei, Jingjing Yang
Water Resources Research (2024) Vol. 60, Iss. 6
Open Access | Times Cited: 4
Metamorphic testing of machine learning and conceptual hydrologic models
Peter Reichert, Kai Ma, Marvin Höge, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 11, pp. 2505-2529
Open Access | Times Cited: 4
Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes
Vinh Ngoc Tran, V. Y. Ivanov, Jongho Kim
Advances in Water Resources (2023) Vol. 182, pp. 104569-104569
Open Access | Times Cited: 11
Predicting Ili River streamflow change and identifying the major drivers with a novel hybrid model
Shuang Liu, Aihua Long, Denghua Yan, et al.
Journal of Hydrology Regional Studies (2024) Vol. 53, pp. 101807-101807
Open Access | Times Cited: 3
On the Sensitivity of Future Hydrology in the Colorado River to the Selection of the Precipitation Partitioning Method
Zhaocheng Wang, Enrique R. Vivoni, Kristen M. Whitney, et al.
Water Resources Research (2024) Vol. 60, Iss. 6
Open Access | Times Cited: 3
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: 3
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
Vinh Ngoc Tran, V. Y. Ivanov, Donghui Xu, et al.
Geophysical Research Letters (2023) Vol. 50, Iss. 17
Open Access | Times Cited: 9
On the need for physical constraints in deep learning rainfall-runoff projections under climate change
Sungwook Wi, Scott Steinschneider
(2023)
Open Access | Times Cited: 7
Resistance of grassland productivity to drought and heatwave over a temperate semi-arid climate zone
Yangbin Huang, Huimin Lei, Limin Duan
The Science of The Total Environment (2024) Vol. 951, pp. 175495-175495
Open Access | Times Cited: 2
Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy
Maddala Madhavi, Ramakrishna Kolikipogu, S. Prabakar, et al.
Remote Sensing in Earth Systems Sciences (2024) Vol. 7, Iss. 4, pp. 642-656
Closed Access | Times Cited: 2
Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau
Wei Deng, Dengfeng Liu, Fengnian Guo, et al.
Agronomy (2024) Vol. 14, Iss. 4, pp. 703-703
Open Access | Times Cited: 1
Assessing the adequacy of traditional hydrological models for climate change impact studies: A case for long-short-term memory (LSTM) neural networks
Jean‐Luc Martel, François Brissette, Richard Arsenault, et al.
(2024)
Open Access | Times Cited: 1
Towards Interpretable Physical‐Conceptual Catchment‐Scale Hydrological Modeling Using the Mass‐Conserving‐Perceptron
Yuan‐Heng Wang, Hoshin V. Gupta
Water Resources Research (2024) Vol. 60, Iss. 10
Open Access | Times Cited: 1
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Bu Li, Ting Sun, Fuqiang Tian, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 20, pp. 4521-4538
Open 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
A review of using deep learning from an ecology perspective to address climate change and air pollution
R. Murugadoss, S. Leena Nesamani, A Banushri, et al.
Global NEST Journal (2024)
Closed Access
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