Scholarships

Geospatial Research Institute Toi Hangarau PhD Scholarship 2022

 

The PhD scholarship (worth $35,000 plus 4,000 for research-related expenses) consists of a UC PhD scholarship (currently $28,000 per annum plus fees) plus a GRI top-up scholarship ($7,000 per annum plus an additional $4,000 for travel or other research-related expenses). More information regarding the scholarship and the regulations of this scholarship can be found on the UC website

 

The GRI PhD scholarship will be offered to candidates applying for one of the projects to be listed below. We also encourage you to get in touch with the project contact person beforehand to discuss the topic and expectations before applying. Your application should be sent to the GRI Manager, Dr Melanie Tomintz (gri-enquiries@canterbury.ac.nz) using the following application form (download application form here). 

 

For the 2022 scholarship, only NZ citizens, NZ permanent residents, or applicants that will be in the country with an appropriate visa by the time of starting the PhD are eligible to apply.

Deadline for applications: 31 March 2022.

 

 


1: Towards automated urban tree inventory for the whole of New Zealand

Title of Project

Towards automated urban tree inventory for the whole of New Zealand

 

UC Senior Supervisor (Project Leader)

Dr Justin Morgenroth, Associate Professor, School of Forestry, University of Canterbury.

 

Project outline

Urban forests are highly valuable to New Zealand’s increasingly urbanised population.  Trees and associated greenspaces make for liveable cities. Managing urban forests to optimise benefits requires a detailed understanding of the location, structure, and diversity of trees at a range of spatial and temporal scales across mixed public/private land ownership.

Current methods for urban forest inventory rely on ground-based data collection which is poorly suited to the task, due to the aforementioned scale and land ownership issues. Remote sensing offers an alternative means of performing large-scale urban forest inventory, but developing rapid and automated methods for extracting tree locations, species, health and other attributes remains an open challenge. Existing approaches often combine customised rulesets and models to identify and assess urban trees using a combination of LiDAR and imagery. Importantly, species classification often requires analysis of hyperspectral data that is expensive and complicated to process.

Recently, convolutional neural networks (deep learning networks) have been successfully applied to high-resolution aerial and satellite imagery for tasks ranging from land-use classification, building and road mapping and agricultural mapping. There is an opportunity to begin testing a framework for large-scale urban tree detection and characterisation using high-resolution aerial imagery and LiDAR data in New Zealand. Developing the training data and methods to automatically extract urban tree information from these datasets paves the way to a national-scale urban tree inventory for New Zealand. Moreover, these methods could be applied to repeated data captures to continuously update our knowledge of New Zealand’s urban trees. This would provide researchers and policymakers with a clear understanding of the pressures and interactions between urban development and the state of our urban forests. Finally, the tree inventory data can be used to estimate the benefits urban forests provide to the residents of New Zealand’s cities.

2: Probabilistic landslide hazard and risk in New Zealand

Title of Project

Probabilistic landslide hazard and risk in New Zealand

 

UC Senior Supervisor (Project Leader)

Dr Tom Robinson, School of Earth & Environment | Te Kura Aronukurangi

 

Other members of the supervision team

Prof Matthew Wilson, School of Earth and Environment, UC

Dr Romy Ridl, KiwiRail

 

Links with organisations outside UC

KiwiRail

 

Outline Vision Mātauranga

In consultation with Tipene Merrit, Kaiārahi Rangahau Māori, there is no direct identifiable connections to VM at this time, as the project is primarily focussed on remote data collection and geospatial modelling, with no mātauranga Māori being used, nor direct involvement of iwi or rūnanga.

Nonetheless, the outputs from this work may be relevant and of interest to Māori communities in the future, in particular both West Coast rūnanga, for whom questions around long-term post-disaster isolation and recovery are pertinent in the context of landslides and infrastructure. We will seek ongoing guidance from UC Kaiārahi around the potential linkages, applications and relevance for Māori of this project throughout its lifespan and at its completion.

This current proposal has strong links to other work by Project Lead Robinson that is codesigning research goals with Te Rūnanga o Ngāi Tahu (TRoNT) focusing on post-earthquake recovery pathways considering future multi-landslide events. This project therefore has potential to work closely with that ongoing work and the potential linkages with TRoNT.

 

Project outline

Over the past 160 years, landslides have claimed significantly more lives in New Zealand than earthquakes, and on average cause $250-300 million in losses each year. With human-induced climate change forecast to increase the rates of landslides over the coming decades, along with the potential for widespread landsliding during future major earthquakes, landslides present an increasing risk to population, infrastructure, and the economy. Nevertheless, landslides remain an under-appreciated hazard in New Zealand and one of the few geophysical hazards for which probabilistic models do not presently exist. Improving the understanding of both the present-day and potential future landslide hazard and risk is therefore a critical research topic that requires urgently addressing.

This PhD project will use of a combination of statistical and geospatial modelling tools to develop separate and combined rainfall- and earthquake-triggered probabilistic landslide hazard models for New Zealand to address three key research questions:

  1. How does ‘background’ annual rainfall-triggered landslide risk in New Zealand compare to the risk from ‘one-off’ large seismic events?
  2. How is landslide risk in New Zealand expected to evolve over the next century based on current climate models?
  3. What effect will the changing climate and increased population/ infrastructure have on the risk posed by landslides?

The project will address these critical questions by combining novel modelling of landslide hazard and risk with existing IPCC ensembles of future climate projections and plausible earthquake scenarios across New Zealand. It will employ methods in geospatial modelling, probabilistic hazard modelling, risk analysis, and climate change and earthquake modelling.

As a direct application, the project will work closely with KiwiRail to understand the risk landslides pose to their network, which is one of the most critical infrastructure links for freight in New Zealand and is particularly exposed to landslides.

3: World-leading Biodiversity Measurement in Aotearoa New Zealand: Machine learning for developing detection models of NZ ecosystem types and restoration, using remote sensing data

Title of Project

World-leading Biodiversity Measurement in Aotearoa New Zealand: Machine learning for developing detection models of NZ ecosystem types and restoration, using remote sensing data

 

UC Senior Supervisor (Project Leader)

Dr John Reid, Ngāi Tahu Research Centre

 

Other members of the supervision team

Prof Matthew Wilson, School of Earth and Environment, UC

 

Links with organisations outside UC

This PhD student would work in close conjunction with the Eco-index Programme, a $3.1 million Research Programme (2020-2024) from the government-funded New Zealand’s Biological Heritage National Science Challenge – Ngā Koiora Tuku Iho. Our mandate is to create a biodiversity index for Aotearoa New Zealand, factoring in both investment in and impact on our terrestrial biodiversity.

 

Outline Vision Mātauranga

The Eco-index programme has taken a treaty-based approach in the development of its biodiversity targets and monitoring. It has strong connections and relationships with iwi, and in particular Ngai Tahu and the Ngāi Tahu Research Centre. The Eco-index involves the development of Eco-indices in partnership with iwi that permit Māori authorities to determine the current state of biodiversity in their takiwā using both conventional and mātauranga Māori derived indicators. Consequently, the project strongly aligns with vision mātauranga. Firstly, in terms of taiao through the development of tools that enable detection of environmental quality from a Māori perspective, and secondly through the innovation theme, where AI and remote sensing technologies are combined with matauranga Māori to generate novel approaches to environmental sensing.

 

Project outline

The project will utilise machine learning and high-fidelity remote sensing data to develop detection models for measuring biodiversity, through ecosystem type and cover. This information will inform a national-level biodiversity index (the Eco-index), which will calculate investment in and impact on biodiversity for Aotearoa New Zealand. We are on the precipice of a new age of remote monitoring made possible through high resolution satellite imagery and technologies such as LiDaR and RADAR. Machine learning and artificial intelligence capabilities can augment these technologies to provide novel insights into the wellbeing of our environment, including our biodiversity. This project will involve developing novel spectral and other signatures using computer vision and deep learning techniques across the remote sensing inputs to classify ecosystem types and areas undergoing native revegetation on production landscapes, across New Zealand. Using visible/near infrared reflectance to measure chlorophyll will also enable assessment of vegetation health and vigor, lake eutrophic levels and so forth. This project will enable realisation of the potential value of these technologies coming together, to provide temporally and spatially accurate data on the extent of ecosystem restoration and revegetation, as a proxy for biodiversity investment outputs. This research project will be highly transdisciplinary, involving data science, GIS, machine learning, and ecology. The student will be located within the wider Eco-index team encompassing more diverse disciplines still, including indicator specialists, social science, data engineering, statistical modelling and engineering. This large and diverse network will ensure the student remains supported, engaged and excited about the work.

4: Integrating spatial ecology and population modelling to understand regional-scale impacts of environmental change on a critical Southern Ocean indicator species, the Adélie penguin

Title of Project

Integrating spatial ecology and population modelling to understand regional-scale impacts of environmental change on a critical Southern Ocean indicator species, the Adélie penguin.

 

UC Senior Supervisor (Project Leader)

Dr. Michelle LaRue, School of Earth and Environment

 

Other members of the supervision team

Dr. Louise Emmerson, Australian Antarctic Division

Prof. Richard Green, UC

 

Links with organisations outside UC

Australian Antarctic Division

University of Tasmania

Maxar Technologies

Antarctica New Zealand

Manaaki Whenua

 

Outline Vision Mātauranga

This project would continue research already begun that involves Māori (developing methods for detection/enumeration of Adélie penguins from high-resolution imagery, a co-developed project between Drs. Phil Lyver and Michelle LaRue, and colleagues) and therefore would be relevant to Māori. This project would extend the initial research and give effect to Vision Mātauranga through Taiao, as understanding the trajectory of an important ocean indicator, penguins, is important for gaining understanding of sustainable resource extraction in the Southern Ocean.

 

Project outline

The Southern Ocean comprises 10% of the world’s ocean and is arguably the most inaccessible, making remote sensing technologies a critical tool for understanding its health, through wildlife monitoring. Adélie penguins are a critical indicator species, as their breeding colonies are distributed around the Antarctic coastline and they prey on krill, which is extracted each year from the Southern Ocean. This transdisciplinary project aims to combine wildlife ecology (through available mark-recapture datasets), spatial science (e.g., habitat suitability modelling), and remote sensing (detection, enumeration, and trend estimation of Adélie penguin colonies in the large region of East Antarctica). The student will collaborate with colleagues at the Australian Antarctic Division, Antarctica New Zealand, and Manaaki Whenua to gain fine-scale, population estimates at dozens of colonies across decades, which will serve as the basis for determining the method for using VHR imagery for understanding indicator species. The student will then work together with colleagues at Maxar Technologies to collect archived and new images for processing and analysis (e.g., advancing machine learning algorithms, such as boosted regression trees or random forests, for automatically detecting colonies; and spectral analysis to determine diet and any detectable population changes across years). Then, the student will compare mark-recapture estimates of population growth and trajectories with concurrent satellite imagery to assess the feasibility and utility of VHR imagery for spatial conservation management of a key indicator species. The resulting abundance and growth rates across a large spatial scale will then serve as the basis for the final piece of analysis, which will involve understanding larger environmental drivers of change (e.g., polynya productivity, ocean temperature, atmospheric cycles) through generalized linear modeling, questions which are not possible to address at smaller scales in the Antarctic. This work will serve as a first to advance such detailed understanding of Adélie penguins across several thousand kilometers of inaccessible Antarctic coastline.

5: Spatially calibrated models for transmission and control of COVID-19 in Aotearoa New Zealand

Title of Project

Spatially calibrated models for transmission and control of COVID-19 in Aotearoa New Zealand.

 

UC Senior Supervisor (Project Leader)

Prof. Malcolm Campbell, School of Earth and Environment and GeoHealth Lab.

 

Other members of the supervision team

Prof. Michael Plank, Mathematics & Statistics

 

Links with organisations outside UC

NZ Ministry of Health and District Health Boards

 

Outline Vision Mātauranga

We appreciate that Māori success is Aotearoa New Zealand’s success.  Whilst the project is not directly focused on Māori, we will endeavour to partner with Māori where results are likely to be of benefit to Māori Hauora.

 

Project outline

There is a need for mathematical models of the spread of Covid-19 through communities that take account of regional and local differences in variables that affect transmission. Consequently, existing models could benefit from geospatial data and methods. Models could be made spatially explicit to include factors like household size, age distribution, vaccination rates, employment types and deprivation index. Some of these factors also affect the effectiveness of public health measures to control the spread of the virus, for example working from home is expected to be less effective in areas with high rates of employment in the hospitality, retail and trade sectors.

This project will develop new spatial models that for the first time in NZ include data on these critical variables mentioned above; calibrated to epidemiological data on cases, hospitalisations and deaths in different areas of NZ. Bringing together existing areas of UC expertise in mathematical modelling and GeoHealth research will enable a transdisciplinary approach to disease modelling; generating wider benefits for health in NZ.

Due to the limited amount of transmission of Covid-19 in NZ to date and the geographically contained natures of the outbreaks, existing models are calibrated primarily to reflect the Auckland population. However, as Covid-19 becomes established in communities throughout the country, there is an increasing need from Ministry of Health and District Health Boards for models that include geographical variation and can inform planning and policy choices. This project will adapt stochastic branching process type models to include age, household structure, vaccination status and other geospatial variables. The outputs will include a multivariate assessment of which places and communities are at risk of high rates of transmission, high disease burden, and/or low effectiveness of public health interventions.

6: A geo-computational modelling framework for national scale wildfire hazard and risk management

Title of Project

A geo-computational modelling framework for national scale wildfire hazard and risk management

 

UC Senior Supervisor (Project Leader)

Dr. Marwan Katurji, Centre for Atmospheric Research, School of Earth and Environment

 

Other members of the supervision team

Prof. Tom Wilson, School of Earth and Environment

Dr. Andres Valencia, Civil and Natural Resources Engineering

 

Links with organisations outside UC

Fire Emergency New Zealand (FENZ)

Mr. Grant Pearce (Senior Fire Scientist, Fire Emergency New Zealand).

Mr. Darrin Woods (National Wildfire Specialist, National Risk Reduction Directorate, Fire Emergency New Zealand)

The project idea has been discussed with both Grant and Darrin and has received immediate interest and priority from FENZ. We have also known that FENZ is currently updating the national vegetation database which is integral to this proposed research as further explained below.

SCION Research – CRI

Dr. Jiawei Zhang (Associate supervisor, Crown Research Institute SCION)

Dr. Tara Strand (Associate supervisor, Crown Research Institute SCION)

 

Outline Vision Mātauranga

Māori set the ethical standards of our aspirational goals in becoming a kaitiaki. The research team also believe and practice a responsibility to protect the urban and natural environment for future generations. Despite a challenging changing climate that might make achieving these goals difficult, it is our intention to make science and knowledge integrated with Mātauranga and better pave the road leading to these goals.

The project’s supervisors through existing collaboration with the CRI Scion, and FENZ will diligently engage with existing social scientists and Māori scientists (mahi rōpū or Māori advisory group) to ensure that the project’s methodology and outputs explore traditional principles and views on indigenous forest management. Although most of the experiments within this project will be numerically geospatial in nature, but our connection with the mahi rōpū will inform researchers on how indigenous vegetation can be used to establish the needed tikanga for these experiments.

 

Project outline

National scale wildfire risk management has become of greater importance in current times due to climate change causing longer and earlier fire seasons, and becoming imperative as the rural-urban development sprawls expose people and assets to dense vegetation and fire hazards (fires of the Port Hills 2017 and Lake Ōhau 2020). Geospatial modelling of wildfire behaviour requires a complex dynamical and statistical two-way spatial integration of weather and climate information with vegetation and topography. Wildfire risk can be quantified with knowledge of the vulnerability of NZ’s built environment from building codes, and forest and rural-urban landscape management. Although efforts have been made to improve the operational performance of fire spread models like Prometheus in New Zealand, no studies have been done to evaluate fire risks across the whole country over climatological periods. This evaluation requires a transdisciplinary team across science, engineering, fire behaviour analysts and managers, and end-user engagement, which is exactly what this research team provides.

This PhD project will develop a national scale geospatial computational framework that incorporates the physical basis of wildfire behaviour and its hazard and risk assessment. Meteorological gridded data (from 26 years of weather simulations provided by MetService) will be used for a climatological study to evaluate fire weather conditions and trends over time.  The second stage of the project will utilize a suite of open source diagnostic and/or data-learning spatial models to downscale meteorological data at NZ’s national digital topography dataset resolution provided by LINZ. The downscaled meteorological data can then be used in a stochastically driven fire spread model integrating a new national  vegetation fuel map information to develop the fire risk evaluation framework. An eventuating interactive spatial modelling tool could be used by fire managers at Fire Emergency New Zealand, and regional councils to build a resilient living environment for future generations.

This project will leverage off two national research programs that the supervisors are investigators in. Resilience to Nature’s Challenges and the MBIE Endeavour 2021-2026 “Extreme wildfire: Our new reality – are we ready?”

7: Seagrass-based blue carbon – is it a red herring or a resilient green option for climate change mitigation?

Title of Project

Seagrass-based blue carbon – is it a red herring or a resilient green option for climate change mitigation?

 

UC Senior Supervisor (Project Leader)

Dr. Mads Thomsen, School of Biological Sciences: Ecologist and expert in seagrasses, blue carbon, droning/remote sensing and spatial analysis.

 

Other members of the supervision team

Ass. Prof. Catherine Reid, School of Earth and Environment: Geologist and expert in sediment analysis, microfossils and stratography.

Dr. Sarah Flannagan, School of Biological Sciences: Molecular Biologist and expert in genetics, bioinformatics, and spatial statistics.

 

Links with organisations outside UC

Dr’s. Lesley Bolton and Melanie Burns, Coastal Scientists, Environment Canterbury: Experts in local ecology of Canterbury with particular interest in climate change mitigation.

Dr. Kim Kelleher, Head of Environment and Sustainability, Lyttelton Port Company.

Prof. T. Wernberg, University of Western Australia: Global expert in impacts from marine heatwaves.

Prof. P. Staerh, University of Aarhus, Denmark: Global expert in seagrasses and modelling.

 

Outline Vision Mātauranga

The project is relevant to Māori and implements Vision Mātauranga through Taiao and Mātauranga – aligning with Ngāi Tahu and the government’s strategic goals for biodiversity, conservation, and climate adaptation. As kaitiakitanga of the environment around Canterbury, Ngāi Tahu seeks to maintain their relationships to places, resources and taonga under new climate conditions, that will carry through their identity. Our approach, which embeds environmental stewardship, aligns with Te Ao Māori and mātauranga Māori and will contribute to outcomes important to Rūnanga, building capability for hapū and participation in monitoring of indicator species. A component of the project is to build capability for monitoring that can be continued by citizen sciences projects and local coastal guardians/hapū/iwi living near seagrass meadows.

Project outline

Burning of fossil fuels and increasing CO2 levels is causing a global climate crisis with severe ecological and socioeconomic impacts from extreme climatic heatwaves, droughts, and storms. Maintaining and increasing CO2 storage from biological organisms is often advocated as viable mitigation.  Marine seagrasses are environmental indicator species that are extremely efficient in storing CO2, and yet virtually nothing is known about this ecosystem service from New Zealand seagrass meadows.  The proposed interdisciplinary PhD-project will combine state of art geospatial analysis and remote sensing techniques, geological sediment analysis of stored carbon, statistical modelling of future seagrass distribution in a warmer world, ecological methods to trial different restoration methods and molecular techniques to identify genotypes with highest carbon uptake, storage, and resilience to future anthropogenic stressors.

Seascape attributes, patch dynamics and resilience to recent extreme climatic events will be analysed from a combination of geotagged images, including digital photos (0.1-100m), drone images (10-10000m) and multiple satellite products (1-100 km) to provide scalable and dynamic maps of seagrass beds in Canterbury. Furthermore, geologic sediment and tissue samples will be analysed for carbon-content and dated from microfossils (e.g., foraminifera) and radiometric methods to convert seagrass distribution and burial rates to large-scale carbon storage services.  Next, ecological distribution modelling and transplantation experiments will explore options for future preservation and expansion of carbon-storage. Finally, molecular, and genetic techniques will use state of art sequencing to identify underpinning genetic stress-responses and phylogenetic relationships within and between seagrass meadows. All sampling – across spatiotemporal scales (cm-100s km) and organizational levels (genetics, ecology, geology) – is geotagged and unified in a spatial GIS framework to analyse how patch and landscape metrics (e.g., perimeter, shapes, and fractal dimensions) may affect observed genetic linkages, resilience, and patch-growth, and, ultimately, carbon storage services.  A highly motivated PhD student will work closely with experts across all these fields and co-develop the research questions and methods to become a truly interdisciplinary scientist with a spatial-analytical underpinning as well as bicultural capacity.

8: The role of geospatial technologies in ESG Reporting of Māori businesses

Title of Project

The role of geospatial technologies in ESG Reporting of Māori businesses

 

UC Senior Supervisor (Project Leader)

Prof. Pavel Castka, UC Business School

 

Other members of the supervision team

Dr. Matthew Rout, Ngāi Tahu Research Centre, UC

 

Project outline

ESG reporting (i.e. reporting on environmental, social and corporate governance) is becoming increasingly important across NZ’s industries for market access and social license to operate. Essential to ESG reporting is providing accurate, balanced and complete account of firms’ performance that goes beyond financial performance. Yet the research demonstrates that the current ESG reporting mechanisms are often inconsistent and lacking veracity – reducing its value proposition for investors, firms and regulatory agencies. In part, the problems are due to the use of technologies – which are often rudimentary and contain significant compliance costs for businesses and government. There are however technologies that can automate these processes, improve accuracy, and significantly reduce compliance costs. Though there is a plethora of technological solutions that can be leveraged to improve ESG reporting, geospatial data is in particular valuable.

With the growth into the processing and export sectors, Māori are increasingly subject to conformance systems and associated compliance burdens. Simultaneously Māori are leading in the development of ESG reporting systems that fit with their indigenous values and resonate with their customers. However, there is a potential to leverage these efforts by incorporating advanced geospatial modelling and advanced technologies for these purposes (i.e. use of satellite imaging, Geographic Information System (GIS), Remote Sensing (RS), Global Positioning System (GPS) and AI). This project will address this gap and develop a technology enhanced approach to compliance that will include Māori values in its design and therefore support transparent monitoring reporting across taiao, oranga, and whai rawa dimensions.

This PhD project will investigate how geospatial data (and related technologies) can be adopted for the purposes of ESG reporting and will focus on critical evaluation of existing technologies and the development of technological solutions in the context of ESG reporting and Māori values. The project is multidisciplinary in nature and will require the student to combine a social science inquiry to analyze practices, policies and regulations related to ESG reporting as well as a geospatial research to provide a technological solution (or a pathway to the solution) based on geospatial modelling. The project will be supervised by Prof Castka and Dr Rout, who lead other projects in this area with assistance from GRI staff to ensure that the student is provided with guidance in the most important aspects of this multidisciplinary project.

9: Distributed, dynamic path planning and control of Unmanned Aerial Vehicle Swarms for geospatial data collection

Title of Project

Distributed, dynamic path planning and control of Unmanned Aerial Vehicle Swarms for geospatial data collection

 

UC Senior Supervisor (Project Leader)

Dr Graeme Woodward, Wireless Research Centre, UC

 

Project outline

The ubiquity of Geospatial Information Systems (GIS) is fuelling a growing need to collect spatial data quickly, efficiently and regularly. This project will develop novel methods to aid the creation of rich and timely data and in so doing will empower people to make information-based decisions.

Many geospatial data collection tasks require many images taken from various perspectives. Unmanned Aerial Vehicles (drones) can be used for this task, but with a single drone it can take several hours before data collection is completed. Furthermore, with a pre-determined flight path, and data post-processing (e.g. synthetic aperture methods based upon a “lawn-mower” flight trajectory), it is often only known after the campaign whether the collected data is of sufficient  quality for the geospatial application at hand, and the data may suffer ‘gaps’, e.g. from feature occlusion.  Instead, we’d like to investigate the use of a drone swarm to speed up capture.  By enabling the drones to co-ordinate in real-time, we believe that completion times can be significantly shortened.  This requires new distributed and scalable drone co-ordination and flight-planning algorithms.  Research will include new real-time collaborative and distributed algorithms for allocating data collection tasks to drones, maintaining the correct spatial relationship between each drone, and for time-efficient path planning based on data observed and current drone positions.  Network connectivity is required to ensure that drones make consistent decisions and to guarantee that the entire swarm can be controlled by a single operator instead of having one operator per drone.  Such communication requirements need to be included into the dynamic path planning algorithm.

The innovations in drone co-ordination and dynamic path planning can be applied to important geospatial sensing and data-collection tasks, including computer vision, RADAR and LIDAR surveying and tracking of targets through complex terrains.


Master of Science Position in River Flood modelling 2021 | GRI – NIWA

 

Download as PDF

 

We have an opening for a motivated student to undertake a Masters research that will improve flood resilience of New Zealand communities through developing methods to improve hydrological model river discharge representation during flood events.

 

One position is available and will be financially supported by NIWA for one year. The Master student will be co-hosted by the Geospatial Research Institute at the University of Canterbury, New Zealand, in conjunction with NIWA. A scholarship of NZ$15,000 is available to successful applicants, with $7,500 starting from December 2021-June 2022, and subject to satisfactory performance, another $7,500 from July 2022-December 2022.

 

The Masters will address aspects of hydrological model river discharge representation from model parameter uncertainty with a focus on flood events. The student will quantify NIWA’s hydrological model parameter sensitivity at the Waikanae catchment during flood events and investigate key model parameter uncertainty based on the Generalised Likelihood Uncertainty Estimation (GLUE) framework. This work will contribute towards improving ensemble flood forecasting in ungauged catchment[1], and hydrodynamics modelling that will also benefit the NIWA-led MBIE National Flood Risk Assessment Endeavour programme[2].

 

Programming literacy (e.g. Matlab, Python or similar), knowledge of hydrological modelling and statistics are highly desirable. Numeracy and excellent written and oral communication skills are essential. Familiarity with the Unix environment would be ideal, the candidate will have the opportunity to run simulations on NIWA’s High Performance Computing System (HPC)[3] which is part of the New Zealand eScience Infrastructure (NeSI).

 

Please direct all enquiries to the project co-supervisors Dr. Celine Cattoen-Gilbert (NIWA Taihoro Nukurangi Celine.Cattoen-Gilbert@niwa.co.nz) and Prof. Matt Wilson (University of Canterbury, matthew.wilson@canterbury.ac.nz). Applications should be sent by email care of the GRI Manager, Dr. Melanie Tomintz (gri-enquiries@canterbury.ac.nz) no later than 3rd December 2021. Please submit the following documents as part of your application:

 

  1. A full curriculum vitae;
  2. A cover letter outlining your motivation and suitability for the research project; and
  3. Contact details of at least two referees.

 

 

Download as PDF

 

 

[1] https://niwa.co.nz/climate/research-projects/river-flow-forecasting

[2] https://niwa.co.nz/natural-hazards/research-projects/m%C4%81-te-haumaru-%C5%8D-te-wai-increasing-flood-resilience-across-aotearoa-0

[3] https://niwa.co.nz/our-services/high-performance-computing-facility/nesi-the-new-zealand-escience-infrastructure