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| Titel |
Model-based evaluation of subsurface monitoring networks for improved efficiency and predictive certainty of regional groundwater models |
| VerfasserIn |
M. J. Gosses, Th. Wöhling, C. R. Moore, R. Dann, D. M. Scott, M. Close |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250065616
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| Zusammenfassung |
| Groundwater resources worldwide are increasingly under pressure. Demands from different
local stakeholders add to the challenge of managing this resource. In response, groundwater
models have become popular to make predictions about the impact of different management
strategies and to estimate possible impacts of changes in climatic conditions. These models
can assist to find optimal management strategies that comply with the various stakeholder
needs. Observations of the states of the groundwater system are essential for the calibration
and evaluation of groundwater flow models, particularly when they are used to guide the
decision making process. On the other hand, installation and maintenance of observation
networks are costly. Therefore it is important to design monitoring networks carefully and
cost-efficiently.
In this study, we analyse the Central Plains groundwater aquifer (~ 4000 km²)
between the Rakaia and Waimakariri rivers on the Eastern side of the Southern
Alps in New Zealand. The large sedimentary groundwater aquifer is fed by the
two alpine rivers and by recharge from the land surface. The area is mainly under
agricultural land use and large areas of the land are irrigated. The other major water use is
the drinking water supply for the city of Christchurch. The local authority in the
region, Environment Canterbury, maintains an extensive groundwater quantity and
quality monitoring programme to monitor the effects of land use and discharges
on groundwater quality, and the suitability of the groundwater for various uses,
especially drinking-water supply. Current and projected irrigation water demand
has raised concerns about possible impacts on groundwater-dependent lowland
streams.
We use predictive uncertainty analysis and the Central Plains steady-state groundwater
flow model to evaluate the worth of pressure head observations in the existing groundwater
well monitoring network. The data worth of particular observations is dependent on the
problem-specific prediction target under consideration. Therefore, the worth of individual
observation locations may differ for different prediction targets. Our evaluation is based on
predictions of lowland stream discharge resulting from changes in land use and irrigation in
the upper Central Plains catchment. In our analysis, we adopt the model predictive
uncertainty analysis method by Moore and Doherty (2005) which accounts for contributions
from both measurement errors and uncertain structural heterogeneity. The method is
robust and efficient due to a linearity assumption in the governing equations and
readily implemented for application in the model-independent parameter estimation
and uncertainty analysis toolkit PEST (Doherty, 2010). The proposed methods
can be applied not only for the evaluation of monitoring networks, but also for the
optimization of networks, to compare alternative monitoring strategies, as well as
to identify best cost-benefit monitoring design even prior to any data acquisition. |
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