Smart Models for Aquifer Management (SAM)
The Smart Models for Aquifer Management (SAM) research programme identified optimal groundwater-surface water flow and transport models to address large-scale, real-time, specific environmental management problems.
Overview
The SAM programme focused on three integrated groundwater–surface water catchments – Hauraki, Ruamahanga and Southland. Covering a diverse range of test catchments ensured that the research outputs would be relevant, workable, and transferable to other catchments across Aotearoa New Zealand.
The project
Freshwater management required a new approach
In 2015, at the outset of the SAM research programme, then-current modelling approaches presented a real risk to adaptive management of New Zealand's aquifers under the National Policy Statement on Freshwater 2014 (NPS-FM 2014).
Interactions between groundwater and surface water systems such as rivers, lakes, wetlands and estuaries are complex. Pre-2015 models simulating these interactions were either too complex and slow to be practical, or lacked necessary integration, or were too simple to be accurate.
Despite these deficiencies, the NPS-FM 2014 required holistic freshwater management that satisfies community aspirations. Such an approach called for integrated groundwater-surface water modelling over larger areas and at finer spatial and temporal scales than ever before.
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More complexity isn’t always better
Modern environmental decision-making is largely based on numerical models. While it is recognised that “uncertainty analysis” should accompany model outputs, models are often far too complex for this to be done. Increasing complexity can increase numerical instability, drain modelling finances and time, and detract from assessment of model output uncertainty.
The SAM programme attempted to treat uncertainty as the fundamental context for environmental modelling, rather than an afterthought, using three key ideas:
- Often, only one side of a predictive uncertainty distribution is of interest: the side that assesses the possibility of unwanted, rather than wanted, events.
- A model that is tuned to testing, and maybe rejecting, the hypothesis that such an event will occur, may not need to be complex, provided it is constructed specifically to explore that particular hypothesis.
- A simple model may contribute to predictive uncertainty through its very simplicity.
Quantifying uncertainty in data availability and integrity can allow decision-makers to become better aware of what models can deliver. Predictions accompanied by a large amount of irreducible uncertainty can be distinguished from those that are not. In the New Zealand land-use management context, such an approach may prompt decision-makers to base policy and/or legislation on model outcomes that have relatively high predictive integrity.
Modelling complex interactions for environmental decision-making
The modelling within the SAM programme followed two pathways:
- best ways to train simple models from complex groundwater models in each catchment
- development of simple model designs that did not need training on a complex model, and that could be built easily in any catchment – and for which an estimate is available of their simplification error through some complex/simple studies in selected catchments
To balance between these two paths, the project was designed to answer questions such as:
- To what extent can parameters employed by a simplified model be informed by measurable characteristics of catchment geological and soil components?
- To what extent must they be informed by local calibration?
- Can parameters of a simplified model inferred through calibration in one catchment be “regionalised” for the use of simplified models in neighbouring catchments?
- What simplification strategies are appropriate for the type of model outcomes being considered? Appropriateness must take account of:
- ability to quantify predictive uncertainty while increasing it as little as possible
- the ability of simplified parameters to be informed by expert knowledge at a variety of scales as much as this as possible
- reduction of calibration-induced bias incurred through inappropriate simplification
- How can calibration-induced predictive bias of a simple model be reduced through adoption of a “simplification-smart” history matching strategy?
Te Whakaheke o Te Wai (2021)
This project focuses on the groundwater systems in Hawke’s Bay’s Heretaunga catchment, and the nature of the water and how it flows through these lands.
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Publications
While SAM ran from 2015 - 2018, publications continue to flow from the programme.
GNS Science reports can be downloaded from the GNS Science Shop(external link).
GNS Science Reports
- Allan, M. 2018 Quantifying uncertainty within an ecologically-coupled lake hydrodynamic model : Lake Wairarapa case study. Lower Hutt, N.Z.: GNS Science.GNS Science report 2018/47. 23 p.; doi:10.21420/J482-WF31(external link)
- Elliott, S.; Rajanayaka, C.; Yang, J.; White, J. 2019 CLUES-GW : a simple coupled steady state surface-groundwater model for contaminant transport. Lower Hutt, N.Z.: GNS Science.GNS Science report 2018/44. 51 p.; doi: 10.21420/34XR-AG12(external link)
- Hemmings, B.J.C.; Knowling, M.J.; Moore, C.R. 2019 Assessing the uncertainty of water quality and lake influx predictions made using complex regional models : Ruamahanga South case study. Lower Hutt, N.Z.: GNS Science.GNS Science report 2019/30. 72 p.; doi: 10.21420/9N73-QE35(external link)
- Hemmings, B.J.C.; Knowling, M.J.; Moore, C.R. 2019 Assessing the uncertainty of water quality and quantity predictions made using complex regional models : Ruamahanga North case study. Lower Hutt, N.Z.: GNS Science.GNS Science report 2019/29. 80 p.; doi: 0.21420/J32Q-W248(external link)
- Howard, S.W.; Griffiths, J.; Zammit, C.; Rouse, H. 2019 Model choice effects on ecological modelling in Mataura River : SAM Programme 2018. Lower Hutt, N.Z.: GNS Science.GNS Science report 2019/05. 55 p.; doi: 10.21420/J32Q-W248(external link)
- Snelder, T. 2018 Nutrient concentration targets to achieve periphyton biomass objectives incorporating uncertainties. Lower Hutt, N.Z.: GNS Science.GNS Science report 2018/38. 41 p.; doi: 10.21420/AJSH-NW16(external link)
- Zammit, C.; Yang, J.; Griffiths, J.; Rajanayaka, C. 2019 Smart models for aquifer management : TopNet modelling suite. Lower Hutt, N.Z.: GNS Science.GNS Science report 2019/27. 118 p.; doi: 10.21420/FBS9-G965(external link)
Postgraduate theses
- Everitt, L.C. 2020 Applications of digital baseflow separation techniques for model validation, Wairarapa valley, New Zealand. Thesis (MSc Physical geography) – Victoria University of Wellington. 165 p; link to full text at: http://researcharchive.vuw.ac.nz/handle/10063/9114(external link)
- Op den Kelder T. 2018 Using predictive uncertainty analysis to optimise data acquisition for stream depletion and land-use change predictions. Thesis (MSc Physical geography and Quaternary geology) – Stockholm University. 78 p; link to full text at: http://www.diva-portal.se/smash/get/diva2:1254304/FULLTEXT01.pdf(external link)
- Rayner, S. 2019 Understanding the potential for nitrate attenuation from paddock to stream using dual nitrate isotopes. Thesis (PhD) – Lincoln University. 185 p; link to full text at: https://researcharchive.lincoln.ac.nz/handle/10182/11449(external link)
Peer reviewed journal articles
- Hemmings, B.; Knowling, M.J.; Moore, C.R. 2020 Early uncertainty quantification for an improved decision support modelling workflow: A streamflow reliability and water quality example. Frontiers in Earth Science. doi:10.3389/feart.2020.565613.
- Doherty, J.; Moore, C.R. 2020 Decision support modeling : data assimilation, uncertainty quantification, and strategic abstraction. Ground water, 58(3): 327-337; doi: 10.1111/gwat.12969(external link); open access available to download at: https://ngwa.onlinelibrary.wiley.com/doi/full/10.1111/gwat.12969(external link)
- Knowling, M.J.; White, J.T.; McDonald, G.W.; Kim, J.-H.; Moore, C.R.; Hemmings, B.J.C. 2020 Disentangling environmental and economic contributions to hydro-economic model output uncertainty: an example in the context of land-use change impact assessment. Environmental Modelling & Software, 127: 104653; doi: 10.1016/j.envsoft.2020.104653(external link); open access available to download at: https://www.sciencedirect.com/science/article/pii/S1364815219305031?via%3Dihub(external link)
- Knowling, M.J.; White, J.T.; Moore, C.R. 2019 Role of model parameterization in risk-based decision support: an empirical exploration. Advances in Water Resources, 128: 59-73; doi: 10.1016/j.advwatres.2019.04.010(external link); open access available to download at: https://www.sciencedirect.com/science/article/pii/S0309170819300909?via%3Dihub(external link)
- Knowling, M.J.; White, J.T.; Moore, C.R.; Rakowski, P.; Hayley, K. 2020 On the assimilation of environmental tracer observations for model-based decision support. Hydrology and Earth System Sciences, 24(4): 1677-1689; doi: 10.5194/hess-24-1677-2020(external link); open access available to download at:
https://www.hydrol-earth-syst-sci.net/24/1677/2020/(external link) - Rayner, S.; Clough, T.J.; Baisden, T.; Moir, J. 2020 Can ruminant urine-N rate and plants affect nitrate leaching and its isotopic composition?. New Zealand journal of agricultural research, 63(1): 87-105; doi: 10.1080/00288233.2019.1648302(external link)
Not open access so would need to be inter-loaned or purchased from the publisher at: https://www.tandfonline.com/doi/full/10.1080/00288233.2019.1648302(external link) - Sarris, T.S.; Close, M.E.; Moore, C.R. 2019 Uncertainty assessment of nitrate reduction in heterogeneous aquifers under uncertain redox conditions. Stochastic Environmental Research and Risk Assessment, 33(8-9): 1609-1627; doi: 10.1007/s00477-019-01715-w(external link); open access full text available at: https://link.springer.com/article/10.1007/s00477-019-01715-w(external link)
- Sarris, T.S.; Scott, D.M.; Close, M.E.; Humphries, B.; Moore, C.R.; Burbery, L.F.; Rajanayaka, C.; Barkle, G.; Hadfield, J. 2019 The effects of denitrification parameterization and potential benefits of spatially targeted regulation for the reduction of N-discharges from agriculture. Journal of environmental management, 247: 299-312; doi: 10.1016/j.jenvman.2019.06.074(external link); not open access so would need to be inter-loaned or purchased through the publisher at: https://www.sciencedirect.com/science/article/pii/S0301479719308825?via%3Dihub(external link)
- Snelder, T.H.; Moore, C.R.; Kilroy, C. 2019 Nutrient concentration targets to achieve periphyton biomass objectives incorporating uncertainties. Journal of the American Water Resources Association, 55(6): 1443-1463; doi: 10.1111/1752-1688.12794(external link); not open access so would need to be inter-loaned or purchased through the publisher at:
https://onlinelibrary.wiley.com/doi/epdf/10.1111/1752-1688.12794(external link) - White, J.T.; Knowling, M.J.; Fienen, M.N.; Feinstein, D.T.; McDonald, G.W.; Moore, C.R. 2020 A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support. Environmental Modelling & Software, 126: article 104657; doi: 10.1016/j.envsoft.2020.104657(external link)
Not open access so would need to be inter-loaned or purchased through the publisher at: https://www.sciencedirect.com/science/article/pii/S1364815219309934?via%3Dihub(external link) - White, J.T.; Knowling, M.J.; Moore, C.R. 2019 Consequences of groundwater-model vertical discretization in risk-based decision making. Ground water, Online first: doi: 10.1111/gwat.12957(external link); not open access so would need to be inter-loaned or purchased through the publisher at: https://ngwa.onlinelibrary.wiley.com/doi/full/10.1111/gwat.12957(external link)
Other publications
- Doherty, J. and Moore C., (2021). Decision Support Modelling Viewed through the Lens of Model Complexity. A GMDSI Monograph. National Centre for Groundwater Research and Training, Flinders University, South Australia. Download here (PDF, 5.3 MB).
Research project details
Primary collaborators: Victoria University of Wellington (VUW), National Institute of Water and Atmospheric Research (NIWA), Market Economics, Institute of Environmental and Scientific Research (ESR)
Additional collaborators: Beef and Lamb, CSIRO, Department of Conservation, Earth in Mind Ltd, Flinders University, Kitson Associates, Landwaterpeople Ltd, Ministry for the Environment, Ravensdown, Tubingen University, University of Waikato, Watermark Numerical Computing
Duration
2015–2018
Funding platform
Status
Completed
Leader
Cath Moore, GNS Science
Funder
Ministry of Business, Innovation & Employment (MBIE)