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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-235
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 May 2018

Research article | 18 May 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Application of an improved global-scale groundwater model for water table estimation across New Zealand

Rogier Westerhoff1,2, Paul White1, and Gonzalo Miguez-Macho3 Rogier Westerhoff et al.
  • 1GNS Science, Taupo, New Zealand
  • 2Deltares, Utrecht, the Netherlands
  • 3University of Santiago de Compostela, Spain

Abstract. Many studies underline the importance of groundwater assessment at the larger, i.e., global, scale. The large-scale models used for these assessments are often simplified and typically not used for smaller-scale, i.e., catchment-scale, studies, because hydrology and water policy are traditionally best constrained at the catchment scale, and because large-scale models are too uncertain for that scale. However, smaller-scale groundwater models can still have considerable uncertainty, especially in data-sparse areas. There is a potential for larger-scale models to constrain the uncertainty for small-scale models. That is because they can provide an extra source of information in data-sparse areas, such as the initial estimate of hydraulic head. Large-scale models, often quick and simple, can thus take away some of the computational burden of local and more sophisticated applications. The problem of this approach is that model uncertainty of large-scale models is often too large, because the quality of their, coarse and global-scale, input data is large, and often inconsistent with the input data of local models. What is needed is an approach where large-scale and local models can meet in the middle.

This study uses an existing, global-scale, groundwater flow model. It feeds that model with national input data of New Zealand terrain, geology, and recharge. It then builds the first New-Zealand national-scale groundwater model. The resulting nationwide maps of hydraulic head and water table depths show that the model points out the main alluvial aquifers with fine spatial detail (200m grid resolution). The national input data and finer spatial detail result in better and more realistic variations of water table depth than the original, global-scale, model outputs. In two regional case studies in New Zealand, the hydraulic head matches the available groundwater level data well. The nationwide water tables show that the model is mostly driven by the elevation (gravity) and impeded by the geology (permeability).

The use of this first New Zealand-wide model can aid in provision of water table estimates in data-sparse regions. The national model can also be used to solve inconsistency of models in areas of trans-boundary aquifers, i.e., aquifers that cover more than one region in New Zealand. Shortcomings of the model are caused by the simplified model properties, but also by the accuracy of input data. Future research should therefore not only focus on further improvements of model equations, but also improved estimation of hydraulic conductivity and the digital elevation model, especially in areas of shallow groundwater level. We further surmise that the findings of this study, i.e., application of a global-scale models at smaller-scales, will lead to subsequent improvement of the global-scale model equations.

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Our study improved a global-scale groundwater model to build the first nationwide estimate of the water table surface in New Zealand. By identifying the main alluvial aquifers with fine spatial detail, we showed that this model can help better delineate aquifer boundaries. In catchment studies we demonstrated excellent correlation with ground observations and provided water table estimates where data were sparse and across regions, which could help solve trans-boundary issues between catchments.
Our study improved a global-scale groundwater model to build the first nationwide estimate of...
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