Farshid Rahmani

Pennsylvania State University — United States

Environmental Science · Physical Sciences

13h-index972citations41works7.42yr avg
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Research Topics

Hydrology and Watershed Management Studies(23), Hydrological Forecasting Using AI(20), Flood Risk Assessment and Management(14), Soil Moisture and Remote Sensing(6), Hydrology and Sediment Transport Processes(4)

Publications41 total

Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
Nature Communications·2025· 1 citedOpen Access
Structural bias should be addressed before effective parameter learning - Insights from SMAP soil moisture simulations using differentiable process-based models
2025Open Access
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models
Journal of Hydrology·2025· 1 cited
Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
2025
Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning
arXiv (Cornell University)·2025Open Access
High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning
Water Resources Research·2025· 20 citedOpen Access
High-Resolution Differentiable Models for Operational National and Global Water Modeling and Assessment
2025Open Access
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models
arXiv (Cornell University)·2025Open Access
Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?
Journal of Hydrology·2024· 5 cited
Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
arXiv (Cornell University)·2024Open Access
High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning
2024· 4 cited
Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations
Journal of Hydrology·2024· 18 cited
Differentiable modeling for global water resources under global change
2024· 1 citedOpen Access
Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning
Water Resources Research·2024· 53 citedOpen Access
Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling
Water Resources Research·2023· 19 citedOpen Access
Differentiable modelling to unify machine learning and physical models for geosciences
Nature Reviews Earth & Environment·2023· 327 citedOpen Access
Improving large-basin river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning
2023· 5 citedOpen Access
Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration
Journal of Hydrology·2023· 41 citedOpen Access
A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds
The Science of The Total Environment·2023· 50 citedOpen Access
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Geoscientific model development·2023· 26 citedOpen Access
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Frequent Co-authors

Chaopeng Shen(34), Kathryn Lawson(27), Jiangtao Liu(16), Tadd Bindas(12), Yalan Song(11), Alison Appling(8), Dapeng Feng(6), Samantha K. Oliver(6), Wen‐Ping Tsai(6), Li Li(4), Martyn Clark(4), Yuchen Bian(4), Ming Pan(4), Yuan Yang(4), Kourosh Behzadian(4), Hylke E. Beck(3), Yegane Khoshkalam(3), Alain N. Rousseau(3), Kian Abbasnezhadi(3), H. Ji(3)