The research activities in the Girotto’s lab are in the broad areas of remote sensing, and data assimilation for improving land surface models. The spatial and temporal variability of the terrestrial hydrology is driven by both natural and anthropogenic processes. While natural processes, such as precipitation induced runoff or evaporation, are included in most global land surface models, anthropogenic processes, such as irrigation, are rarely modeled. Satellite observations are one of the great sources to monitor the hydrological cycle in its entirety. The goal of this project is to implement, run, and evaluate irrigation schemes within the catchment land surface model.
The student will help on the evaluation and analysis of the various irrigation schemes. The evaluation will use in-situ, and satellite-collected data of various hydrology quantities including soil moisture, runoff, groundwater, and evapotranspiration. The student will be guided in learning the research process as well as remote sensing data analysis using data processing software such as Python, MATLAB, R, etc.
Interest in hydrology (natural and human driven processes), remote sensing and modeling. Basic knowledge of some programming language or willingness to learn. Experience with coursework or research in environmental science, hydrology, remote sensing.