Jiangtao Liu

I am interested in using multiple satellite datasets, in-situ observation datasets, and reanalysis products to investigate how climate variability and human activities affect water resources. My approach integrates physics-based hydrological models with deep learning techniques, ensuring that model predictions remain both accurate and physically interpretable. In parallel, I develop BERT/GPT-based foundation models that can be fine-tuned for tasks such as streamflow forecasting, soil moisture prediction, and water quality assessment. Ultimately, I aim to deliver robust, scalable, and transparent modeling frameworks that guide decision-making from local watersheds to global scales, helping mitigate risks such as droughts, floods, and landslides in a changing climate.