Coastal hydrology of the Mississippi delta based on rapid-repeat synthetic aperture radarCoastal hydrology of the Mississippi delta based on rapid-repeat synthetic aperture radar

Job Details

The Mississippi River Delta (MRD), the 7th largest delta on Earth, drains about 41% of the contiguous United States into the Gulf of Mexico. Its sustainability is endangered because of rapid land loss due to relative sea level rise, a condition that will challenge all major temperate-zone deltas this century. Yet despite the overall net land loss, some areas of the MRD are gaining land. This project provides an opportunity to further the understanding of the hydrological and biological processes driving deltaic land gain or loss using NASA’s latest remote sensing technology. Located in Pasadena, California, JPL has a campus-like environment situated on 177 acres in the foothills of the San Gabriel Mountains and offers a work environment unlike any other: we inspire passion, foster innovation, build collaboration, and reward excellence.

Responsibilities

To assess the vulnerability and future risk of a delta it is necessary to understand the processes by which the deltaic landscape can build ground elevation. Low elevation land within coastal deltas are subject to complex hydrology driven by both tides and river flow. The water carries sediment and carbon, and its local inundation regimen dictates the type and health of vegetation. It is known that the availability of sediments varies significantly between seasons, and that plants, with their structural characteristics, can have a significant impact on deposition rates and on the development of the small channel network that carries sediment into the interior of islands and wetlands. The posted position is in support of the Delta-X, a newly-funded NASA Earth Venture Suborbital-3 project. In this project, we seek to quantify the relative contributions and feedbacks of two dominant processes contributing to elevation: 1) deposition of inorganic sediments and 2) accumulation of organic material produced by plants. The project combines data acquired in airborne remote sensing campaigns in the MRD with state-of-the art physical modeling. Current numerical hydrodynamic models (e.g., Delft3D) enable simulation of the flow of water, sediment load and deposition at the scale of hydrological connectivity (an ecogeomorphic zone). Knowledge of the temporal and spatial characteristics of water flow across the landscape is required to develop realistic models suitable for differentiating the fundamental conditions that dictate whether an area will accrete soil sufficiently fast to offset the combination of subsidence and sea level rise. We are seeking a postdoctoral researcher to work with the UAVSAR synthetic aperture radar data to map the hydrological network and to determine water level change using short-temporal-repeat datasets. The candidate will develop, implement, and test algorithms to 1) determine the spatial and temporal patterns of water flow and 2) map the water channel network within the wetlands. Development and implementation of methods for automated detection and correction of InSAR phase unwrapping errors will be a critical component of the work. In addition, the researcher is expected to write manuscripts suitable for peer-reviewed publication and to present results at professional meetings. Dr. Cathleen E. Jones will serve as the postdoctoral advisor. The appointee will carry out research in collaboration with the JPL advisor and others, resulting in publications in the open literature. The selected candidate will work within a large team of multidisciplinary researchers from several institutions and is expected to contribute to a stimulating, dynamic, and inclusive research environment. Other JPL Collaborators: Marc Simard, Ernesto Rodriguez

Qualifications

Candidates should have a recent PhD in a related field with substantial experience in synthetic aperture radar data analysis. Training and experience with SAR remote sensing data processing methods is required and experience with InSAR processing methods is highly desired. Candidates should have excellent abilities in programming in a Unix environment (python and/or C++ preferred). Knowledge of AI methods/deep learning and prior experience processing UAVSAR data will be viewed favorably. GIS tool experience (e.g. QGIS) and experience with GitHub is desired as well. Candidates who have received their PhD within the past five years since the date of their application are eligible. Postdoctoral Scholar positions are awarded for a minimum of one-year period and may be renewed up to a maximum duration of three years.

 

Candidates should submit the following to this site: CV, representative publications, contact information for three references, and a cover letter stating their research accomplishments and interests.

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