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Development of objective function-based ensemble model for streamflow forecasts

Lin, Y., Wang, D., Zhu, J., Sun, W., Shen, C., & Shangguan, W. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.130861 · Cited by 20 · see citing works

Synergizing Intuitive Physics and Big Data in Deep Learning: Can We Obtain Process Insights While Maintaining State‐Of‐The‐Art Hydrological Prediction Capability?

He, L., Shi, L., Song, W., Shen, J., Wang, L., Hu, X., & Zha, Y. (2024) · Water Resources Research · doi:10.1029/2024wr037582 · Cited by 18 · see citing works

A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions

Le, M., Kim, H., Xuan, H., Beling, P. A., & Lakshmi, V. (2024) · Advances in Water Resources · doi:10.1016/j.advwatres.2024.104694 · Cited by 18 · see citing works

Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool

Hah, D., Quilty, J., & Sikorska‐Senoner, A. E. (2022) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2022.105474 · Cited by 18 · see citing works

FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications

Tabas, S. S., Humaira, N., Samadi, S., & Hubig, N. (2023) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2023.105854 · Cited by 17 · see citing works

Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model

Duong, T. D., Tran, V. N., & Nguyen, V. T. (2025) · Water Resources Management · doi:10.1007/s11269-025-04231-5 · Cited by 16 · see citing works

AI Improves the Accuracy, Reliability, and Economic Value of Continental‐Scale Flood Predictions

Tran, V. N., Kim, T., Xu, D., Tran, H., Le, M., Tran, T., Kim, J., Tran, T. D., Wright, D. B., Restrepo, P. J., et al. (2025) · AGU Advances · doi:10.1029/2025av001678 · Cited by 16 · see citing works

Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

Kwon, Y., Cha, Y., Park, Y., & Lee, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-36439-z · Cited by 16 · see citing works

Enhancing Hydrological Extremes Forecasting Capabilities in Data‐Scarce Regions Through Transfer Learning With Data Augmentation

Tang, Y., Tang, X., Zhu, Z., Gao, C., Liu, L., Zhao, F., & Zhang, S. (2025) · Earth s Future · doi:10.1029/2025ef006060 · Cited by 15 · see citing works

A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models

Robles, K. P. V., Solmerin, J. G., Pugat, G. C. E., & Monjardin, C. E. F. (2026) · Water · doi:10.3390/w18010119 · Cited by 14 · see citing works

A Systematic Review of Deep Learning Applications in Streamflow Data Augmentation and ForecastingPreprint

Sit, M. A., Demiray, B. Z., & Demir, İ. (2022) · doi:10.31223/x5hm08 · Cited by 14 · see citing works

Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?

Khoshkalam, Y., Rousseau, A. N., Rahmani, F., Shen, C., & Abbasnezhadi, K. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.132540 · Cited by 13 · see citing works

Streamflow Estimation in Ungauged Regions using Machine Learning: Quantifying Uncertainties in Geographic ExtrapolationPreprint

Le, M., Kim, H., Adam, S., Xuan, H., Beling, P. A., & Lakshmi, V. (2022) · doi:10.5194/hess-2022-320 · Cited by 13 · see citing works

A Mass Conservation Relaxed (MCR) LSTM Model for Streamflow Simulation Across CONUS

Wang, Y., Zhang, L., Erichson, N. B., & Yang, T. (2025) · Water Resources Research · doi:10.1029/2024wr039131 · Cited by 12 · see citing works

Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S.

Yang, Y., Pan, M., Feng, D., Xiao, M., Dixon, T., Hartman, R. K., Shen, C., Song, Y., Sengupta, A., Monache, L. D., et al. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-5453-2025 · Cited by 12 · see citing works

Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal

Marasini, U., & Pokhrel, M. (2024) · Discover Civil Engineering · doi:10.1007/s44290-024-00084-w · Cited by 11 · see citing works

Enhancing the performance of runoff prediction in data-scarce hydrological domains using advanced transfer learning

Chen, S., Mao, Q., Feng, Y., Li, H., Ma, D., Zhao, Y., Liu, J., & Cheng, H. (2024) · Resources Environment and Sustainability · doi:10.1016/j.resenv.2024.100177 · Cited by 11 · see citing works

A generalised hydrological model for streamflow prediction using wavelet Ensembling

Panda, C., Panda, K. C., Singh, R. M., Singh, R., & Singh, V. P. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.132883 · Cited by 11 · see citing works

A novel remote sensing-based calibration and validation method for distributed hydrological modelling in ungauged basins

Zheng, D., Zhu, W., Han, Y., & Lv, A. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133119 · Cited by 11 · see citing works

Coupled SWAT, Stationary Wavelet Transform, and Interpretable Machine Learning to Improve Watershed Streamflow Simulation

Ren, C., Chang, J., Wang, X., Niu, C., Wang, L., & Zhang, J. (2025) · Water Resources Management · doi:10.1007/s11269-025-04117-6 · Cited by 10 · see citing works

Dynamic hydrological flow prediction with self-iterative spatiotemporal graph neural network: Modeling long- and short-period topological dynamics

Xue, L., & Zhu, Y. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.134122 · Cited by 10 · see citing works

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Zwart, J. A., Diaz, J., Hamshaw, S. D., Oliver, S. K., Ross, J. C., Sleckman, M. J., Appling, A., Corson-Dosch, H. R., Jia, X., Read, J. S., et al. (2023) · Frontiers in Water · doi:10.3389/frwa.2023.1184992 · Cited by 10 · see citing works

HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network

Nguyen, V. T., Tran, V. N., Tran, H., Bình, Đ. V., Duong, T. D., Dang, T. D., & Ebeling, P. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.102994 · Cited by 10 · see citing works

Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification

Willard, J., & Varadharajan, C. (2025) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2025jh000732 · Cited by 9 · see citing works

Investigating Deep Learning Knowledge Transfer in Streamflow Prediction From Global to Local Catchment

Ougahi, J. H., & Rowan, J. S. (2026) · Water Resources Research · doi:10.1029/2025wr041194 · Cited by 9 · see citing works

Conceptual hydrological model-guided SVR approach for monthly lake level reconstruction in the Tibetan Plateau

Hou, M., Wei, J., Chu, H., Shi, Y., Ayantobo, O. O., Xu, J., Zhu, X., & Ren, Y. Q. (2022) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2022.101271 · Cited by 9 · see citing works

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep LearningPreprint

Xiang, Z., & Demir, İ. (2022) · doi:10.31223/x5bw6r · Cited by 8 · see citing works

Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation

Li, P., Song, Y., Pan, M., Lawson, K., & Shen, C. (2025) · Hydrology and Earth System Sciences · doi:10.5194/hess-29-6829-2025 · Cited by 7 · see citing works

Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models

Bohl, J. P., Wood, R. R., Frank, C., Astagneau, P. C., Peters, J., & Brunner, M. I. (2026) · Hydrology and earth system sciences · doi:10.5194/hess-30-4667-2026 · Cited by 6 · see citing works

Fast urban flood modeling informing response decisions: Model development and future perspectives

Duan, T., Li, P., Huang, P., & Wei, W. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104152 · Cited by 6 · see citing works

Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS

Quilty, J., & Jahangir, M. S. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.134986 · Cited by 5 · see citing works

Toward routing river water in land surface models with recurrent neural networks

Lima, M., Deck, K. M., Dunbar, O. R. A., & Schneider, T. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-3145-2025 · Cited by 5 · see citing works

Improving trans-regional hydrological modelling by combining LSTM with big hydrological data

Tang, S., Sun, F., Zhang, Q., Singh, V. P., & Yao, F. (2025) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2025.102257 · Cited by 5 · see citing works

Regionalization of Hydrologic Behavior and Pothole Water Storage Dynamics in the Prairie Pothole Region

Rahmani, J., Shen, C., & Ameli, A. (2026) · Water Resources Research · doi:10.1029/2025wr040280 · Cited by 4 · see citing works

Foundation‐Scale Satellite Embeddings Reframe Hydrological Generalization as a Representation Problem

Ou, Z., & Zheng, Y. (2026) · Geophysical Research Letters · doi:10.1029/2025gl121604 · Cited by 4 · see citing works

Multiple conceptual hydrological models for simulating streamflow in data-sparse river basins: an application of the Vietnamese Cau river basin

Tuan, T. T. (2024) · Water Practice & Technology · doi:10.2166/wpt.2024.181 · Cited by 4 · see citing works

How much historical data do we need? The role of data recency and training period length in LSTM-based rainfall-runoff modeling

Yu, Q., & Tolson, B. A. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.135046 · Cited by 3 · see citing works

Geospatial Foundation Embeddings as Transferable Catchment Descriptors

Liu, J., Shen, C., Yang, Y., Ji, H., Lawson, K., & Kraabel, N. (2026) · Geophysical Research Letters · doi:10.1029/2026gl122814 · Cited by 3 · see citing works

Towards more accurate streamflow and flood prediction via explainable CNN-LSTM: leveraging ERA5 gridded precipitation and hydrological observations through input-architecture co-design

Huang, S., Nan, T., & Yueling, W. A. N. G. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.135700 · Cited by 3 · see citing works

The representation of rivers in operational ocean forecasting systems: a review

Matte, P., Wilkin, J., & Staneva, J. V. (2025) · State of the Planet · doi:10.5194/sp-5-opsr-19-2025 · Cited by 3 · see citing works

Daily runoff prediction for ungauged basins: Optimization of model transfer and selection of donor basins

Song, X., Yi, S., Liu, H., Shen, Y., & Lu, F. (2025) · Science China Earth Sciences · doi:10.1007/s11430-024-1630-7 · Cited by 3 · see citing works

Comparison of Multiple Indirect Approaches to Estimate Streamflow in the Osage and Severn Rivers

Vatanchi, S. M., & Maghrebi, M. F. (2025) · Journal of Hydrologic Engineering · doi:10.1061/jhyeff.heeng-6409 · Cited by 2 · see citing works

Role of the water balance constraint in the long short-term memory network: large-sample tests of rainfall-runoff predictionPreprint

Li, Q., & Zhao, T. (2024) · doi:10.5194/egusphere-2023-2841 · Cited by 2 · see citing works

无资料流域日径流预测: 模型迁移优化及参考流域决策分析

宋, 昕., Yi, S., Liu, H., Yalan, S., & Lu, F. (2025) · SCIENTIA SINICA Terrae · doi:10.1360/sste-2024-0299 · Cited by 1 · see citing works

Challenges of modelling climate change impacts on hydrology and water resources: AI is the game changer—a review

Onyutha, C. (2025) · Environmental Research Climate · doi:10.1088/2752-5295/ae2a60 · Cited by 1 · see citing works

A hyperparameter tuning strategy for an LSTM model to simulate reservoir outflows: large-scale evaluation across 441 dams in the CONUS

Basirifard, M., Zhang, J., Cao, J., Liu, X., Hong, Y., & Yang, T. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.136204 · Cited by 1 · see citing works

GRHyMoLAP: A process-driven ODE catchment hydrology model inspired by GR4J and HyMoLAP approaches

Houénafa, S. E., Latella, M., Gohouede, L. C., & Sezen, C. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.135597 · Cited by 1 · see citing works

Comment on hess-2022-113

(2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-113-rc1

Comment on hess-2022-113

(2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-113-cc1

Reply on RC2

Mai, J. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-113-ac2

Reply on RC1

Mai, J. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-113-ac1

Comment on hess-2022-111

(2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-111-rc1

Comment on hess-2022-111

(2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-111-rc2

Comment on hess-2022-245

Feng, D., Beck, H. E., Lawson, K. E., et al. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-245-cc1

Comment on hess-2022-113

(2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-113-rc2

Reply on RC2

Sun, A. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2022-111-ac2

Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning

Ayzel, G., Kurochkina, L., Казаков, Э. Э., & Zhuravlev, S. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202016301001 · Cited by 19 · see citing works

Leveraging Flow Duration Information to Improve Streamflow Prediction in Data-sparse Regions with Deep Learning Models

Feng, D., & Shen, C. (2020) · AGU Fall Meeting Abstracts

Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modelingPreprint

Feng, D., Lawson, K., & Shen, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13380 · Cited by 3 · see citing works

Closing the data gap: runoff prediction in fully ungauged settings using LSTMPreprint

Hashemi, R., Javelle, P., Delestre, O., & Razavi, S. (2023) · doi:10.5194/hess-2023-282 · Cited by 4 · see citing works

Transfer Learning Using Inaccurate Physics Rule for Streamflow Prediction

Bao, T., Johnson, T. T., & Jia, X. (2024) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2024/793