Phd Opportunities (3)
A new generation of hydrologic models for remote catchments worldwide.
PhD top-up scholarships available on an Australian Research Council Discovery Project (details below) to be supervised collaboratively across three Australian Universities, including opportunities for travel and learning in reputed institutions overseas. Stipend includes a significant top-up over and above Australian RTP scholarships for eligible domestic students or residents.
Why it matters:
The World Bank states that the largest economic risk facing us over a 10-year horizon is a "Global Water Crisis鈥. While some facets of such a crisis may be beyond our control, its potential impact can be mitigated through proper modelling, prediction and communication. This research addresses a key factor impeding hydrologic modelling, prediction and communication, seeking to utilize the power of hydrologic modelling under uncertainty along with derived or indirect streamflows to measure and model river flow worldwide. Success in this research will lead to a many-fold increase in hydrologic measuring capability worldwide, as fewer than 1% of catchments are presently gauged.
Supervision team:
- Ashish Sharma (UNSW)
- Lucy Marshall (University of Sydney)
- Hae Na Yoon (Newcastle University)
- Seokhyeon Kim (Kyung Hee University, South Korea)
Who we are looking for:
Students with passion on using/refining/generating surrogate hydrologic data using the latest advances in Satellite remote sensing. Students with a passion on developing a new generation of hydrologic models that do not require calibration using ground streamflow measurements. Students must be excellent with computers and managing large datasets, be comfortable with uncertainty concepts (statistics) and have a demonstrated knowledge of hydrology. Three PhD scholarships available.
How to apply:
Please e-mail Professor Ashish Sharma (a.sharma@unsw.edu.au) with (a) residency status (preference is Australian residents), (b) CV, (c) transcripts. Please note applicants must have honours 1 or equivalent, or sufficient research experience to be eligible for an RTP.
Australian Research Council Discovery Project Details:
DP250101254 A Bayesian model for inferred streamflow in absence of in-situ observations.聽
A novel Bayesian framework for specifying hydrological models when no streamflow measurements exist is proposed. The framework uses a new likelihood function that operates with inferred, scaleless measurements of streamflow, enabling use of satellite reflectance and altimetry as surrogates of streamflow, while incorporating hydrologic signatures to introduce scale. A new temporal differencing-based reflectance surrogate overcomes deficiencies in existing alternatives, the framework enabling semi-distributed estimation for high order catchments. Streamflow data from Australian Hydrologic Reference Stations are to be used to assess the viability of the proposed framework, before application to ungauged catchments in remote settings worldwide.聽