Quick link to the deepLDB.org Landslide project website

Quick link to the soil moisture forecast website

Quick link to Our team on AGU TV

 

Research Interests

Our research focuses on advancing fundamental understanding of the interactions between hydrology and other subsystems (e.g., ecosystem, energy and carbon cycles, solid earth and channels). Water scarcity and excess create varied conflicts and competitions in different parts of the world, and drastic changes in the water cycle put stress on natural and societal systems. Importantly, the changes in water states and flows are a significant driver for changes in other systems. We strive to provide sound physical science, produced by data, data-driven and process-based models, to support decision-making across multiple scales, from catchment to global scales. Meanwhile, our fundamental understanding of the hydrologic cycle, after decades of research, still remains much to be improved. We strive to identify commonalities and learn underlying principles.

Our primary methods include (1) state-of-the-art deep learning (DL); and (2) physically-based hydrologic models. The former through mining land-based and remotely-sensed data, help to efficiently generate hypotheses about how the system functions, while the latter allows us to conduct experiments. Recently, we have focused on DL-based prediction of soil moisture, streamflow, landslides and other variables. The DL has also manifested refreshingly strong predictive capability for many applications. Please read my argument, review, and opinions for the integration of deep learning in water-related fields.

We are opening sourcing our hydrologic deep learning code.

Besides machine learning, another tool we use intensively is the Process-based Adaptive Watershed Simulator (PAWS), a comprehensive, computationally-efficient parallel hydrologic model designed for large-scale simulation. The model is now coupled to the Community Land Model (CLM), and therefore is able to simulate Carbon/Nitrogen cycling, ecosystem dynamics and their interactions with the water cycle. Due to its comprehensiveness, efficiency, and flexibility, this tool provides a useful platform for the integration of biogeochemistry, fluid mechanics, and human dimensions into a uniform modeling framework, to investigate their mutual interactions, to test hypothesis about causal relationships and to assess future changes.

PAWS is now open to all users. READ repo access will be granted to anyone who
request it by sending an email to cshen@engr.psu.edu.

 

Recent news

Dr. Wen-Ping Tsai's paper on parameter learning is accepted in Nature Communications! We believe parameter learning and differentiable hydrology represent the next evolutionary stage of hydrology and machine learning.

Shen's book chapter Applications of Deep Learning in Hydrology is now online.

A major achievement on connecting physics and machine learning is to be published online. Check back here recently!

PhD student Dapeng Feng's GRL paper, on streamflow prediction in continuously data-sparse regions, is now online. Our code (find link above) trains such a prediction system for CAMELS data in about an hour.

PhD student Farshid Rahmani's first paper on stream temperature modeling is online.

Through our volunteer work, our group has just brought online a deep-learning-based tool to track soil moisture changes in Africa/Asia to help combat the locust swarms which threatens global food security.

We appreciate our funding organizations:

National Science Foundation presents FY 2021 budget request | NSF - National  Science FoundationSC Logos | U.S. DOE Office of Science (SC)

 

Past News

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