Chaopeng Shen

Affiliate Researcher
Titles and Affiliations
Associate Professor, Civil and Environmental Engineering

Research Summary

Areas of Interest: Large scale computational hydrology Land surface processes Water-carbon-nutrient interactions under global change Hydrologic scaling issues High performance subsurface reactive transport modeling

In the News

Research Keywords

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Projects

Recent Publications

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Feng, D., Beck, H., Lawson, K. & Shen, C., Jun 30 2023, In: Hydrology and Earth System Sciences. 27, 12, p. 2357-2373 17 p.

Research output: Contribution to journalArticlepeer-review

Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration

Khoshkalam, Y., Rousseau, A. N., Rahmani, F., Shen, C. & Abbasnezhadi, K., Jul 2023, In: Journal of Hydrology. 622, 129682.

Research output: Contribution to journalArticlepeer-review

Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats

Liu, J., Hughes, D., Rahmani, F., Lawson, K. & Shen, C., Mar 17 2023, In: Geoscientific Model Development. 16, 5, p. 1553-1567 15 p.

Research output: Contribution to journalArticlepeer-review

How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent?

Mangukiya, N. K., Sharma, A. & Shen, C., Jul 2023, In: Hydrological Processes. 37, 7, e14936.

Research output: Contribution to journalArticlepeer-review

Hazard assessment framework for statistical analysis of cut slopes using track inspection videos and geospatial information

Palese, M., Pei, T., Qiu, T., Zarembski, A. M., Shen, C. & Palese, J. W., 2023, (Accepted/In press) In: Georisk.

Research output: Contribution to journalArticlepeer-review

Applying Knowledge-Guided Machine Learning to Slope Stability Prediction

Pei, T., Qiu, T. & Shen, C., Oct 1 2023, In: Journal of Geotechnical and Geoenvironmental Engineering. 149, 10, 04023089.

Research output: Contribution to journalArticlepeer-review

A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds

Saha, G. K., Rahmani, F., Shen, C., Li, L. & Cibin, R., Jun 20 2023, In: Science of the Total Environment. 878, 162930.

Research output: Contribution to journalArticlepeer-review

Differentiable modelling to unify machine learning and physical models for geosciences

Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M., Fenicia, F., Kifer, D., Li, L., Liu, X., Ren, W., Zheng, Y., Harman, C. J., Clark, M., Farthing, M., Feng, D., Kumar, P., Aboelyazeed, D., Rahmani, F., & 11 othersSong, Y., Beck, H. E., Bindas, T., Dwivedi, D., Fang, K., Höge, M., Rackauckas, C., Mohanty, B., Roy, T., Xu, C. & Lawson, K., Aug 2023, In: Nature Reviews Earth and Environment. 4, 8, p. 552-567 16 p.

Research output: Contribution to journalArticlepeer-review

Hybrid forecasting: blending climate predictions with AI models

Slater, L. J., Arnal, L., Boucher, M. A., Chang, A. Y. Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., Villarini, G., Wilby, R. L., Wood, A. & Zappa, M., May 15 2023, In: Hydrology and Earth System Sciences. 27, 9, p. 1865-1889 25 p.

Research output: Contribution to journalReview articlepeer-review

Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation

Chen, X., Wang, S., Gao, H., Huang, J., Shen, C., Li, Q., Qi, H., Zheng, L. & Liu, M., Dec 2022, In: Journal of Hydrology. 615, 128562.

Research output: Contribution to journalArticlepeer-review

The Data Synergy Effects of Time-Series Deep Learning Models in Hydrology

Fang, K., Kifer, D., Lawson, K., Feng, D. & Shen, C., Apr 2022, In: Water Resources Research. 58, 4, e2021WR029583.

Research output: Contribution to journalArticlepeer-review

Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy

Feng, D., Liu, J., Lawson, K. & Shen, C., Oct 2022, In: Water Resources Research. 58, 10, e2022WR032404.

Research output: Contribution to journalArticlepeer-review

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Liu, J., Rahmani, F., Lawson, K. & Shen, C., Apr 16 2022, In: Geophysical Research Letters. 49, 7, e2021GL096847.

Research output: Contribution to journalArticlepeer-review

Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin

Liu, B., Tang, Q., Zhao, G., Gao, L., Shen, C. & Pan, B., May 1 2022, In: Water (Switzerland). 14, 9, 1429.

Research output: Contribution to journalArticlepeer-review

Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Nagendra, S., Kifer, D., Mirus, B., Pei, T., Lawson, K., Manjunatha, S. B., Li, W., Nguyen, H., Qiu, T., Tran, S. & Shen, C., 2022, In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15, p. 4349-4370 22 p.

Research output: Contribution to journalArticlepeer-review