Tsunami detection by high-frequency radar in British Columbia: performance assessment of the time-correlation algorithm for synthetic and real events

The authors recently proposed a new method for detecting tsunamis using high-frequency (HF) radar observations, referred to as “time-correlation algorithm” (TCA; Grilli et al. Pure Appl Geophys 173(12):3895–3934, 2016a, 174(1): 3003–3028, 2017). Unlike standard algorithms that detect surface current patterns, the TCA is based on analyzing space-time correlations of radar signal time series in pairs of radar cells, which does not require inverting radial surface currents. This was done by calculating a contrast function, which quantifies the change in pattern of the mean correlation between pairs of neighboring cells upon tsunami arrival, with respect to a reference correlation computed in the recent past. In earlier work, the TCA was successfully validated based on realistic numerical simulations of both the radar signal and tsunami wave trains. Here, this algorithm is adapted to apply to actual data from a HF radar installed in Tofino, BC, for three test cases: (1) a simulated far-field tsunami generated in the Semidi Subduction Zone in the Aleutian Arc; (2) a simulated near-field tsunami from a submarine mass failure on the continental slope off of Tofino; and (3) an event believed to be a meteotsunami, which occurred on October 14th, 2016, off of the Pacific West Coast and was measured by the radar. In the first two cases, the synthetic tsunami signal is superimposed onto the radar signal by way of a current memory term; in the third case, the tsunami signature is present within the radar data. In light of these test cases, we develop a detection methodology based on the TCA, using a correlation contrast function, and show that in all three cases the algorithm is able to trigger a timely early warning.     

Mobilization of colloidal ferrihydrite particles in porous media—An inner-sphere complexation approach

Prediction of colloid release and transport as affected by reactive species remains a significant challenge for field applications. In this paper, we report experimental and modeling results of ferrihydrite colloid release under the influence of citrate species. Using a 3-plane surface complexation model, equilibrium constants were obtained for the three proposed inner-sphere complexes by fitting a citrate adsorption isotherm on ferrihydrite at pH 4, and a pH adsorption envelop with 0.64 mM citrate. The constants were used in a reactive transport model for simulating reaction fronts of dissolved species during injection of citrate in ferrihydrite-coated quartz columns. Simulation results show that sorption alone may not adequately describe the breakthrough curves. Inclusions of ferrihydrite dissolution and re-adsorption of Fe(III) improve the prediction of dissolved species transport. Additionally, matrix diffusion may be needed for improved prediction. For the release of colloidal iron oxides it was shown that both oxide dissolution and interfacial repulsion controlled the process during complete breakthrough. However, the peak release of colloids, which occurred during the actual breakthrough of dissolved species, was mainly brought about by electric double layer forces. These particles underwent detachment-deposition-detachment cycles along the flow path, and emerged in the effluent with the major reaction front. To quantitatively predict colloid release, a semi-empirical linear correlation was established, linking the calculated electric potential to experimental colloid release rates. The model may be applied to the prediction and scaling of aquifer remediation studies involved in the injection of organic ligands to mobilize particle bound contaminants.     

Stratal architecture and platform evolution of an early Frasnian syn‐tectonic carbonate platform,Canning Basin,Australia

Facies architecture and platform evolution of an early Frasnian reef complex in the northern Canning Basin of north‐western Australia were strongly controlled by syn‐depositional faulting during a phase of basin extension. The margin‐attached Hull platform developed on a fault block of Precambrian basement with accommodation largely generated by movement along the Mount Elma Fault Zone. Recognition of major subaerial exposure and flooding surfaces in the Hull platform (from outcrop and drillcore) has enabled comparison of facies associations within a temporal framework and led to identification of three stages of platform evolution. Stage 1 records initial ramp development on the hangingwall dip slope with predominantly deep subtidal conditions that prevented any cyclic facies arrangements. This stage is characterised by basal siliciclastic deposits and a major deepening‐upward facies pattern that is capped by a sequence boundary towards the footwall (north‐west) and a major flooding surface towards the hangingwall. Stage 2 reflects the bulk of platform aggradation, significant platform growth towards the hangingwall and the development of reef margins and cyclic facies arrangements. Thickening of this stage towards the hangingwall indicates that accommodation was generated by rotation of the fault block and overlying platform. Stage 3 records a major flooding and backstep of the platform margin. The Hull platform illustrates important elements of margin‐attached carbonate platforms in a half‐graben setting, including: (i) prominent, but limited, coarse siliciclastic input that does not have a major detrimental effect on carbonate production near the rift margin in arid to semi‐arid settings; (ii) wedge‐shaped accommodation created by syn‐depositional rotation of fault blocks and tilting of the hangingwall dip slope, resulting in shallow‐water facies and subaerial exposure up‐dip of the rotational axis and deeper water facies down‐dip; and (iii) evolution of a ramp to rimmed shelf, coincident with a sequence boundary–flooding surface, that is accelerated by tilting of the hangingwall dip slope during fault‐block rotation.     

A multidimensional fuzzy least-squares regression approach for estimating hydraulic gradients in unconfined aquifer formations and its application to the Gulf Coast aquifer in Goliad County,Texas

Epistemic uncertainties arise during the estimation of hydraulic gradients in unconfined aquifers due to planar approximation of the water table as well as data gaps arising from factors such as instrument failures and site inaccessibility. A multidimensional fuzzy least-squares regression approach is proposed here to estimate hydraulic gradients in situations where epistemic uncertainty is present in the observed water table measurements. The hydraulic head at a well is treated as a normal (Gaussian) fuzzy variable characterized by a most likely value and a spread. This treatment results in hydraulic gradients being characterized as normal fuzzy numbers as well. The multidimensional fuzzy least-squares regression has an exact analytical form and as such can be implemented easily using matrix algebra methods. However, the method was noted to be sensitive to round-off and truncation errors when the epistemic uncertainties are small. A closeness index based on the cardinality of a fuzzy number is used to evaluate how well the regression model fits the fuzzy hydraulic head observations. A fuzzy Euclidian distance measure is used to compare two fuzzy numbers and to evaluate how fuzziness in the observed hydraulic heads affects the fuzziness in the estimated hydraulic gradients. The Euclidian distance measure is also used to ascertain the influence of each well on the fuzzy hydraulic gradient estimation. The fuzzy regression framework is illustrated by applying it to evaluate hydraulic gradients in the unconfined portion of the Gulf Coast aquifer in Goliad County, TX. The results from the case-study indicate that there is greater uncertainty associated with the estimation of the hydraulic gradients in the vertical (Z-axis) direction. The epistemic uncertainties in the hydraulic head data at the wells have a significant impact on the gradient estimates when they are of the same order of magnitude as the most likely values of the observed heads. The influence analysis indicated that 5 of the 13 wells in the network had a critical influence on at least one of the hydraulic gradients. Three wells along the northeastern section of the study area and bordering the Victoria County were noted to have the least influence on the regression estimates. The fuzzy regression framework along with the associated goodness-of-fit and influence measures provides a useful set of tools to characterize the uncertainties in the hydraulic heads and gradients arising from data gaps and planar water table approximation.     

A multi-period optimization model for conjunctive surface water–ground water use via aquifer storage and recovery in Corpus Christi,Texas

The present study develops and evaluates a decision support system for the conjunctive management of the current surface and proposed aquifer storage and recovery (ASR) facility of the city of Corpus Christi, TX using a simulation–optimization approach. The objective of the model is to maximize water storage in the surface and subsurface storage units while meeting (1) the freshwater inflow requirements to the Corpus Christi estuary and (2) the water demands of the city and its service area. The model is parameterized using streamflow data from the U. S. Geological Survey gauging stations on the Nueces River and its tributaries as well as long-term climatic data and regional hydrogeologic information. Results indicate that a single-well field ASR facility is capable of storing approximately 925 ha-m (7,500 ac-ft) of water over a 5-year period in the Evangeline Aquifer with a total potential storage of about 2,715 ha-m (22,000 ac-ft) of water over the jurisdictional area of the Corpus Christi Aquifer Storage and Recovery Conservation District. Surplus surface water sources are seen to contribute approximately 49–96 % of the water stored in the ASR during the simulation period. The remaining storage came from either Choke Canyon Reservoir or Lake Corpus Christi, which also resulted in a slight reduction in evapotranspiration in both reservoirs. The analysis indicates that the proposed ASR system is not limited on the supply side but multiple well fields may be required to increase the storage capacity within the aquifer.     

Combined optimization of a wind farm and a well field for wind-enabled groundwater production

Energy requirements constitute a significant cost in groundwater production and can also add to a large carbon footprint when fossil fuels are used for power. Wind-enabled water production is advantageous as it minimizes air pollution impacts associated with groundwater production and relies on a renewable resource. Also, as groundwater extraction represents a deferrable load (i.e., it can be carried out when energy demands within an area are low), it provides a convenient way to overcome the intermittency issue associated with wind power production. Multiple turbine wind farms are needed to generate large quantities of power needed for large-scale groundwater production. Turbines must be optimally located in these farms to ensure proper propagation of kinetic energy throughout the system. By the same token, well placement must reconcile the competing objectives of minimizing interferences between production wells while ensuring the drawdowns at the property boundary are within acceptable limits. A combined simulation–optimization based model is developed in this study to optimize the combined wind energy and water production systems. The wind farm layout optimization model is solved using a re-sampling strategy, while the well field configuration is obtained using the simulated annealing technique. The utility of the developed model is to study wind-enabled water production in San Patricio County, TX. Sensitivity analysis indicated that identifying optimal placement of turbines is vital to extract maximum wind power. The variability of the wind speeds has a critical impact on the amount of water that can be produced. Innovative technologies such as variable flow pumping devices and aquifer storage recovery must be used to smooth out wind variability. While total groundwater extraction is less sensitive to uncertainty in hydrogeological parameters, improper estimation of aquifer transmissivity and storage characteristics can affect the feasibility of wind-driven groundwater production.