A signature-based approach to quantify soil moisture dynamics under contrasting land-uses

Advisor: Dr. H. McMillan

Model diagnostic tools for soil moisture data (‘soil moisture signatures‘) has been increasingly developed. However, the soil moisture signatures have been tested under limited types of land-uses, which must be a strong control factor on the dynamics. Then, my question becomes, Are soil moisture signatures useful to describe the different dynamics under contrasting land-uses? We summarized 9 soil moisture signature behaviors for 12 different land-use types.

Impacts of deforestation and oil palm plantation on the runoff-processes in humid tropical Sumatra, Indonesia

PI: Dr. T. Sayama, Funded by Kaken

To assess the impact of deforestation on floods in Indonesia, we first need to understand the rainfall-runoff processes. Our team found an interesting phenomenon in the humid tropical hillslope: groundwater tables respond quickly with large magnitude, despite the hillslope being characterized by a thick clayey soil layer, which is typically highly impermeable. We investigated the mechanisms through fieldwork and modelings.

A framework to predict hydrologic processes at continental scales

PI: Dr. H. McMillan, Funded by NSF

Our team suggest hydrologic signature, a metrics that represent watershed dynamics,  as a new approach to estimate dominant processes. We investigate the links between hydrologic signatures and watershed process using large-scale streamflow dataset from the U.S., U.K., Brazil, and Australia.

Snowmelt processes in the National Water Model during extreme Atmospheric River events

Advisor: Dr. H. McMillan, Dr. Ming Pan, Dr. Ty Brandt, Dr. Edwin Sumargo, Dr. Forest Cannon at CW3E, Funded by NOAA

National Water Model (NWM) is a hydrologic model that simulates the streamflow over the entire continental United States. The project aims to assess and improve the prediction of rapid snowmelt caused by Rain-on-Snow events in the Sierra Nevada and Cascade mountains by NWM.

I worked on reviewing literature, compiling hydro-climatic data, and analyzing the soil moisture data. Moreover, I created a real-time webmap to visualize the rain-on-snow risks to inform decision-makers.

How hydrologysts perceive watersheds? A survey on perceptual models

PI: Dr. H. McMillan

Our team is investigating on how hydrologists perceive watershed systems by analyzing their conceptual illustrations in literature. Such illustrations are called ‘perceptual models,’ and are often used as blueprints of computational models. We explore the best guidelines to create perceptual models for the better knowedge sharing in hydrology.

Missing the main load? Quantifying marine debris loadings from storm drain and river margin sources in the San Diego River

PI: Dr. H. McMillan, Dr. Trent Biggs, Funded by NOAA

Debris from rivers is a major contaminant in the San Diego coastal areas. The project team is conducting fieldwork, laboratory, and modeling works in the San Diego River watershed to quantify and identify the source of marine debris. I supervise three undergraduate students and leading a fieldwork module in the project.

Canyon flow analysis with particle velocity imagery (PIV) technique in San Diego

PI: Dr. H.McMillan, [Movie for outreaching]

Traditional measurements of river flow rates require contact with the flow. With the PIV (particle image velocimetry) technique, we can obtain the river surface’s 2-D velocity field without entering the river. PIV cross-correlates the particle in sequential images of the flowing river surface. Out group aims to assess the effectiveness and uncertainties of the PIV technique in small rivers (the river width around less than 10 metres).

I supervised two undergraduate students on the setting up field sites, implementing the fieldwork, and analyzing the data with PIV.

Isotope streamflow analysis in semi-arid urban region, San Diego

PI: Dr. H. McMillan

Our group worked on the identifying source of water in highly modified urban rivers in arid San Diego with isotope analysis EMMA. I assisted the data cleaning and literature review about uncertainty in EMMA procedure.