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Projects

Smarter Targeting of Erosion Control (STEC) MBIE research programme (2018-2023)

  • Extension of the LUMASS modelling framework to a low-code interoperable model coupling framework (Herzig et al. 2020)
  • Implementation of the sediment generation component of the Temporal Erosion and Sediment Transport (TEST) model in LUMASS .
  • Coupling of the sediment generation component with the sediment transport (in rivers) component (NIWA) using LUMASS' interoperable model coupling framework.

New land-use map and automatic update procedure for Horizons Regional Council (2020)

  • Implementation of an automatic update procedure for the new land-use map utilising LUMASS’ spatial modelling framework.
  • This includes the automatic generation of a detailed data provenance record of each processing step and its parameters to produce the land-use map.

Modelling suspended sediment baselines and reductions required to achieve freshwater objectives for Southland using SedNetNZ (2020)

  • Application of the core SedNetNZ model implemented in the LUMASS modelling framework. including downstream sediment routing and sediment trapping in lakes

Interoperability project of the Our Land and Water National Science Challenge (2019-2020)

  • Development of an interoperability wrapper around the LUMASS engine. This exposes any spatial process or optimisation model developed within the framework through the Basic Model Interface (BMI) and enables it integration into any BMI-compliant composite model. (Elliot et al. 2020)

  • Extension of the spatial optimisation component to enable incentivised optimisation, e.g. accounting for payments for environmental contaminant reductions. (Herzig et al. 2020b)

Streaming environmental modelling data into web-based Virtual Reality (2019)

  • LUMASS-based prototype streaming server for live model data into mobile virtual realitay applications (WebVR) (Herzig 2019, Herzig & Scholten 2019 (Presentation | Video)

Innovative Data Analysis (IDA) MBIE programme (2014-2018)

  • Implementation of fine-grained data (model) provenance tracking into the LUMASS modelling framework. This extension provides automatic PROV-N encoded data provenance tracking for any LUMASS model each time it is executed within the LUMASS framework. (Spiekermann et al. 2019, Herzig et al. 2019)

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