The External Engagement Program: Skills and Knowledge Audit characterises both current and future water modelling workforce skill and knowledge needs.
This report has been produced as part of the Queensland Water Modelling Network’s (QWMN) External Engagement Program (EEP) and is intended to fulfil the review and reporting requirements of the EEP’s Skills and Knowledge Audit project. The report represents the assessment and views of the authors and should not be interpreted as the views of the QWMN or the various stakeholders that contributed information and ideas to this report. This report is provided to guide conversations and identify potential solutions. Further analysis and integration with knowledge developed from wider EEP initiatives is required to identify and develop appropriate responses to the issues identified.
The overarching scope of the skills and knowledge audit was to characterise both current and future water modelling workforce skill and knowledge needs. Attitudes of different types of organisation (i.e., 1. Government; 2. Consulting; 3. Research/Higher Education) within the water modelling sector were captured. Within and between these types of organisations a distinction between “providers” and “clients” was also made when analysing results. The “provider” category included individuals and organisations that develop models and/or produce model-derived information. The “client” category refers to individuals and organisations that use model results to inform their activities.
Data collection and collation was achieved via four key mechanisms:
Information developed through these activities was synthesised to provide a snap shot of the current approach to development of the Queensland water modelling workforce as well as identifying future development needs and emerging issues.
Results suggest that there are mixed views within the water modelling profession about the current state of workforce skills and knowledge development. Feedback from individuals in consulting organisations and model provider categories suggest that, while there are areas that could be improved, overall the water modelling workforce in Queensland is stable and that there are no urgent issues in the areas of training and skills development that are limiting the performance of these parts of the sector. A different view emerged from both the government and research/higher education organisations and within the model client category. Individuals and organisations in these groups reported significant challenges in recruiting professionals with experience in both the technical aspects of model application and broader knowledge of the policy and regulatory environments in which model-based information is used. It was observed that these groups often engage with more complex multi-disciplinary modelling activities which often require a capacity that can only be developed over many years. This combination of skills, knowledge and experience across different sectors and policy / regulatory environments is considered valuable and rare in the current workforce.
Results revealed that new professionals to government and consulting organisations are primarily sourced from local undergraduate university programs in mathematics, physics, physical sciences, engineering, planning and architecture. No single undergraduate training program was identified as advantageous over another. The national university system appears to be providing graduates with the necessary skills to enter the workforce but there are significant opportunities to enhance the quality of Australian-trained graduates. Specifically the majority (65%) of organisations interviewed reported a decline in the overall skills and knowledge of new graduates with declines in mathematics (60% of organisations) and physical process understanding (75% of organisations) specifically noted. A trend towards decreasing knowledge of the basic processes of model development, calibration, scenario simulation and evaluation of outputs was reported. There were also reported challenges for the recruitment of suitably qualified Australian candidates to enter research and higher education roles as either doctoral students or post-doctoral researchers – there were too few suitable qualified domestic candidates and so typically international applicants have to be sought.
Interviews revealed that the dominant mechanism for providing ongoing training and skills development within these organisations was via internal communities of practice (CoP) that exist both formally and informally within organisations and between organisations within the same sector. These CoP are characterised by a readily identifiable practice leader or champion, an internal “best practice” document and the extensive use of mentors. Many CoPs also make extensive use of on-line systems for sharing information and solving problems. Incorporation of these approaches and systems into future training initiatives is seen as desirable. The use of short courses offered by external training providers (e.g., software developers) as a mechanism for training the modelling workforce was reported to be in decline.
A wide range of emerging issues were identified. A need to better develop the programming, data analysis and visualisation/communication capacity of the workforce – particularly for working with “big data” has been highlighted as essential across all organisations as well as the provider-client spectrum. A need to improve the sector’s approach to succession planning and knowledge transfer in the modelling workforce was also identified – particularly in government and research/higher education organisations. A range of options to address these issues were identified including innovative cross-organisation internships for early- and mid- career professionals as well as development of new collaborative training opportunities delivered by consortiums of the consulting industry, government and higher education providers. Different initiatives could be tailored for both model providers and model client groups within the sector with a specific need to focus on the government-client group. These ideas merit further exploration.
More broadly, while the Queensland water modelling community is well positioned to take advantage of technological innovations to improve its overall capacity, there is a widespread call for additional investment in the science that underpins the models that are applied right across the industry. This is needed to ensure there is continual improvement in modelling capacity and outputs. Specifically, improved knowledge of the processes that drive water and water quality in natural systems is needed so that models can be advanced and trust in model-derived information is maintained and enhanced in the broader community. There is also a sector-wide recognition that there are still substantial challenges in access data for modelling projects. In many instances the time delays associated with accessing data make the data effectively useless (i.e., in many instances relevant data is not able to be provided in time to be considered by a given project or decision making process). All organisation types within the water modelling community have expressed an urgent need to develop effective on-line data repositories that are easily accessible by all potential users in the water modelling profession. How to best respond to these issues warrants further consideration and investigation.
It should also be noted that the information developed through this project needs to be interpreted with the limitations of the data collection approach in mind. Specifically the findings of this report will contain some bias inherent in the interview methodology. The results are also developed from the views of relatively senior professionals in the field and this may influence the perceptions of factors such as what is achievable/expected from undergraduate training programs, the capacity of young professionals and the importance of succession of knowledge. There is also an under-representation of the views of the model client category in the information developed. Further research could address some of these issues.