Numerical simulation of transient groundwater age distributions assisting land and water management in the Middle Wairarapa Valley, New Zealand

2016 ◽  
Vol 52 (12) ◽  
pp. 9430-9451 ◽  
Author(s):  
Michael W. Toews ◽  
Christopher J. Daughney ◽  
Fabien J. Cornaton ◽  
Uwe Morgenstern ◽  
Ryan D. Evison ◽  
...  
Keyword(s):  
Water Management ◽  
Numerical Simulation ◽  
New Zealand ◽  
Groundwater Age ◽  
Land And Water Management

Revised hydrogeological model of the hydrothermal system Waiwera (New Zealand)

2021 ◽  
Author(s):  
Andreas Grafe ◽  
Thomas Kempka ◽  
Michael Schneider ◽  
Michael Kühn
Keyword(s):  
Water Management ◽  
New Zealand ◽  
Water Level ◽  
Hot Water ◽  
Hydrogeology Journal ◽  
Natural State ◽  
Geothermal System ◽  
Geological Model ◽  
Species Transport ◽  
Study Region

<p>The geothermal hot water reservoir underlying the coastal township of Waiwera, northern Auckland Region, New Zealand, has been commercially utilized since 1863. The reservoir is complex in nature, as it is controlled by several coupled processes, namely flow, heat transfer and species transport. At the base of the aquifer, geothermal water of around 50°C enters. Meanwhile, freshwater percolates from the west and saltwater penetrates from the sea in the east. Understanding of the system’s dynamics is vital, as decades of unregulated, excessive abstraction resulted in the loss of previously artesian conditions. To protect the reservoir and secure the livelihoods of businesses, a Water Management Plan by The Auckland Regional Council was declared in the 1980s [1]. In attempts to describe the complex dynamics of the reservoir system with the goal of supplementing sustainable decision-making, studies in the past decades have brought forth several predictive models [2]. These models ranged from being purely data driven statistical [3] to fully coupled process simulations [1].<br><br>Our objective was to improve upon previous numerical models by introducing an updated geological model, in which the findings of a recently undertaken field campaign were integrated [4]. A static 2D Model was firstly reconstructed and verified to earlier multivariate regression model results. Furthermore, the model was expanded spatially into the third dimension. In difference to previous models, the influence of basic geologic structures and the sea water level onto the geothermal system are accounted for. Notably, the orientation of dipped horizontal layers as well as major regional faults are implemented from updated field data [4]. Additionally, the model now includes the regional topography extracted from a digital elevation model and further combined with the coastal bathymetry. Parameters relating to the hydrogeological properties of the strata along with the thermophysical properties of water with respect to depth were applied. Lastly, the catchment area and water balance of the study region are considered.<br><br>The simulation results provide new insights on the geothermal reservoir’s natural state. Numerical simulations considering coupled fluid flow as well as heat and species transport have been carried out using the in-house TRANSport Simulation Environment [5], which has been previously verified against different density-driven flow benchmarks [1]. The revised geological model improves the agreement between observations and simulations in view of the timely and spatial development of water level, temperature and species concentrations, and thus enables more reliable predictions required for water management planning.<br><br>[1] Kühn M., Stöfen H. (2005):<br>      Hydrogeology Journal, 13, 606–626,<br>      https://doi.org/10.1007/s10040-004-0377-6<br><br>[2] Kühn M., Altmannsberger C. (2016):<br>      Energy Procedia, 97, 403-410,<br>      https://doi.org/10.1016/j.egypro.2016.10.034<br><br>[3] Kühn M., Schöne T. (2017):<br>      Energy Procedia, 125, 571-579,<br>      https://doi.org/10.1016/j.egypro.2017.08.196<br><br>[4] Präg M., Becker I., Hilgers C., Walter T.R., Kühn M. (2020):<br>      Advances in Geosciences, 54, 165-171,<br>      https://doi.org/10.5194/adgeo-54-165-2020<br><br>[5] Kempka T. (2020):<br>      Adv. Geosci., 54, 67–77,<br>      https://doi.org/10.5194/adgeo-54-67-2020</p>

scholarly journals Calibration of a transient transport model to tritium data in streams and simulation of groundwater ages in the western Lake Taupo catchment, New Zealand

2013 ◽  
Vol 17 (3) ◽  
pp. 1217-1227 ◽  
Author(s):  
M. A. Gusyev ◽  
M. Toews ◽  
U. Morgenstern ◽  
M. Stewart ◽  
P. White ◽  
...  
Keyword(s):  
New Zealand ◽  
Transport Model ◽  
Groundwater Age ◽  
Uniform Grid ◽  
Groundwater System ◽  
Small Surface ◽  
Streams And Rivers ◽  
Groundwater Levels ◽  
Transient Transport ◽  
Western Lake

Abstract. Here we present a general approach of calibrating transient transport models to tritium concentrations in river waters developed for the MT3DMS/MODFLOW model of the western Lake Taupo catchment, New Zealand. Tritium has a known pulse-shaped input to groundwater systems due to the bomb tritium in the early 1960s and, with its radioactive half-life of 12.32 yr, allows for the determination of the groundwater age. In the transport model, the tritium input (measured in rainfall) passes through the groundwater system, and the simulated tritium concentrations are matched to the measured tritium concentrations in the river and stream outlets for the Waihaha, Whanganui, Whareroa, Kuratau and Omori catchments from 2000–2007. For the Kuratau River, tritium was also measured between 1960 and 1970, which allowed us to fine-tune the transport model for the simulated bomb-peak tritium concentrations. In order to incorporate small surface water features in detail, an 80 m uniform grid cell size was selected in the steady-state MODFLOW model for the model area of 1072 km2. The groundwater flow model was first calibrated to groundwater levels and stream baseflow observations. Then, the transient tritium transport MT3DMS model was matched to the measured tritium concentrations in streams and rivers, which are the natural discharge of the groundwater system. The tritium concentrations in the rivers and streams correspond to the residence time of the water in the groundwater system (groundwater age) and mixing of water with different age. The transport model output showed a good agreement with the measured tritium values. Finally, the tritium-calibrated MT3DMS model is applied to simulate groundwater ages, which are used to obtain groundwater age distributions with mean residence times (MRTs) in streams and rivers for the five catchments. The effect of regional and local hydrogeology on the simulated groundwater ages is investigated by demonstrating groundwater ages at five model cross-sections to better understand MRTs simulated with tritium-calibrated MT3DMS and lumped parameter models.

scholarly journals Measuring Crustal Seismic Anisotropy through Shear Wave Splitting

2021 ◽  
Author(s):  
◽  
Kenny Graham
Keyword(s):  
Numerical Simulation ◽  
New Zealand ◽  
Shear Wave ◽  
Seismic Anisotropy ◽  
Shear Wave Splitting ◽  
Anisotropic Structure ◽  
Crustal Anisotropy ◽  
Physical Mechanisms ◽  
Alpine Fault ◽  
Wave Splitting

<p>This thesis involves the study of crustal seismic anisotropy through shear wave splitting. For the past three decades, shear wave splitting (SWS) measurements from crustal earthquakes have been utilized as a technique to characterize seismic anisotropic structures and to infer in situ crustal properties such as the state of the stress and fracture geometry and density. However, the potential of this technique is yet to be realized in part because measurements on local earthquakes are often controlled and/or affected by physical mechanisms and processes which lead to variations in measurements and make interpretation difficult. Many studies have suggested a variety of physical mechanisms that control and/or affect SWS measurements, but few studies have quantitatively tested these suggestions. This thesis seeks to fill this gap by investigating what controls crustal shear-wave splitting (SWS) measurements using empirical and numerical simulation approaches, with the ultimate aim of improving SWS interpretation. For our empirical approach, we used two case studies to investigate what physical processes control seismic anisotropy in the crust at different scales and tectonic settings. In the numerical simulation test, we simulate the propagation of seismic waves in a variety of scenarios.  We begin by measuring crustal anisotropy via SWS analysis around central New Zealand, where clusters of closely-spaced earthquakes have occurred. We used over 40,000 crustal earthquakes across 36 stations spanning close to 5.5 years between 2013 and 2018 to generate the largest catalog of high-quality SWS measurements (~102,000) around the Marlborough and Wellington region. The size of our SWS catalog allowed us to perform a detailed systematic analysis to investigate the processes that control crustal anisotropy and we also investigated the spatial and temporal variation of the anisotropic structure around the region. We observed a significant spatial variation of SWS measurements in Central New Zealand. We found that the crustal anisotropy around Central New Zealand is confined to the upper few kilometers of the crust, and is controlled by either one mechanism or a combination of more than one (such as structural, tectonic stresses, and gravitational stresses). The high correspondence between the orientation of the maximum horizontal compressive stress calculated from gravitational potential energy from topography and average fast polarization orientation around the Kaikōura region suggests that gravitationally induced stresses control the crustal anisotropy in the Kaikōura region. We suggest that examining the effect of gravitational stresses on crustal seismic anisotropy should not be neglected in future studies. We also observed no significant temporal changes in the state of anisotropy over the 5.5 year period despite the occurrence of significant seismicity.   For the second empirical study, we characterized the anisotropic structure of a fault approaching failure (the Alpine Fault of New Zealand). We performed detailed SWS analysis on local earthquakes that were recorded on a dense array of 159 three-component seismometers with inter-station spacing about 30 m around the Whataroa Valley, New Zealand. The SWS analysis of data from this dense deployment enabled us to map the spatial characteristics of the anisotropic structure and also to investigate the mechanisms that control anisotropy in the Whataroa valley in the vicinity of the Alpine Fault. We observed that the orientation of the fast direction is parallel to the strike of the Alpine Fault trace and the orientations of the regional and borehole foliation planes. We also observed that there was no significant spatial variation of the anisotropic structure as we move across the Alpine Fault trace from the hanging wall to the footwall. We inferred that the geological structures, such as the Alpine Fault fabric and foliations within the valley, are the main mechanisms that control the anisotropic structure in the Whataroa valley.    For our numerical simulation approach, we simulate waveforms propagating through an anisotropic media (using both 1-D and 3-D techniques). We simulate a variety of scenarios, to investigate how some of the suggested physical mechanisms affect SWS measurements. We considered (1) the effect on seismic waves caused by scatterers along the waves' propagation path, (2) the effect of the earthquake source mechanism, (3) the effect of incidence angle of the incoming shear wave. We observed that some of these mechanisms (such as the incidence angle of the incoming shear wave and scatterers) significantly affect SWS measurement while others such as earthquake source mechanisms have less effect on SWS measurements. We also observed that the effect of most of these physical mechanisms depends on the wavelength of the propagating shear wave relative to the size of the features. There is a significant effect on SWS measurements if the size of the physical mechanism (such as scatterers) is comparable to the wavelength of the incoming shear wave. With a larger wavelength, the wave treats the feature as a homogeneous medium.</p>

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