PhD Position in Groundwater Hydrology and Machine Learning
Explore how machine learning can augment traditional hydrologic models to predict groundwater baseflow in snow-influenced watersheds. Develop and test integrated computational models to improve water resource management under climate variability.
AI-generated overview
Project Description
Project Overview
The PhD project focuses on predicting baseflow in snow-dominated watersheds using a combination of physics-based hydrologic models and machine learning. It will explore hydrologic modeling advancements to better understand water flow and interactions in these sensitive environments.
What You Will Do
The selected student will conduct research within the Shuai Computational and Integrated Hydrology Group, developing computational models that integrate machine learning approaches with traditional hydrologic modeling to predict groundwater baseflow patterns.
Expected Outcomes
This project aims to produce improved predictive tools for groundwater baseflow in snow-affected watersheds. The outcomes should address challenges in hydrologic forecasting and aid water resource management under changing climatic conditions.
Why This Matters
Understanding and predicting groundwater dynamics in snow-dominated regions is crucial for water resource planning and management. This research can enhance modeling accuracy, support climate adaptation strategies, and contribute to sustainable watershed management.
Entry Requirements
How to Apply
Eligibility
Supervisor Profile
Dr. Pin Shuai is a hydrologist specializing in groundwater-surface water interactions, flow and reactive transport, and biogeochemical processes. He leads the Shuai Computational and Integrated Hydrology Group at Utah State University, focusing on combining modeling approaches to explore hydrologic phenomena. His work is well-cited in the hydrology community, reflecting his contributions to understanding riverbank denitrification and the impact of hydrologic processes on water quality.