Project title: Groundwater flow model
The components
The groundwater flow model framework in NZWaM-Hydro (the NZWaM groundwater model) is a collection of models: it consists of a nationwide deployment of a simplified groundwater model by GNS Science (Equilibrium Water Table); a NIWA Integrated surface and groundwater model (TopNet-GW); and where available more advanced groundwater models (e.g. MODFLOW).
The role of the NZWaM groundwater model is to improve surface water model simulations, by estimating groundwater storages and groundwater fluxes. Improving simulations can provide more realistic discharge simulation in groundwater dependent environment.
To develop the NZWaM groundwater model the project will bring together data in the hydro-geofabric, our understanding of losing and gaining streams, and water-table fluctuations from the underlying GNS Science and NIWA models.
The objective
This project aims to improve the accuracy of our hydrological models discharge predictions, at both catchment and regional scales. This will require coupling of surface water models with groundwater flow models, including water age simulations as a complement of the transport processes.
This task also aims to improve process conceptualisation and the ability to reproduce different catchment-scale hydrological signatures such as: water quantity, water age, groundwater response to water use and climate change.
The project is funded through the Strategic Science Investment Fund from the Ministry of Business, Innovation and Employment.
Planned tasks for the year
- Complete TopNet-GW parametrisation and regionalisation
- Implement geological derived parameters in TopNet(-GW) for New Zealand alongside model benchmarking.
- Conceptualise and test in one catchment. This will be developed with input and feedback from the project Stakeholder Reference Group
Progress to date
- Provision of gaining and losing stream information as modelled with the Equilibrium Water Table model;
- Development of a framework to couple surface water and groundwater flux generation, including uncertainty propagation within both components.
- Updated national static gaining/losing stream classification GIS layers (both observed and modelled).
- Published a gaining and losing stream classification paper for peer review publication
Outputs
Yang, J., Griffiths, J. and Zammit, C., National classification of surface–groundwater interaction using random forest machine learning technique. River Research and Applications. https://doi.org/10.1002/rra.3449, 2019.