Abstract. A methodology to optimize the design of an offshore tsunami network array is presented, allowing placement of sensors to be used in a Early Tsunami Warning System framework. The method improves on previous methods by including multiple tsunami parameters as a measure of the predictive accuracy through a single cost function. The use of different tsunami parameters allows for a network which is less subject to biases found when using a single parameter. The resulting network performance was tested against an historical event, suggesting that having such a network in place could have provided meaningful information for the hazard assessment. The low number of sensors required may be useful in implementing such networks in places where funding of denser arrays might be of concern.
A methodology to optimize the design of an offshore tsunami network array is presented, allowing determination of the placement of sensors to be used in a tsunami early warning system framework. The method improves on previous sensor location methods by integrating three commonly used tsunami forecast performance indicators as a measure of the predictive accuracy through a single cost function. The joint use of different tsunami parameters allows for a network that is less subject to bias found when using a single parameter. The resulting network performance was tested using a set of synthetic target scenarios and also verified against a model of the 2014 Pisagua event, suggesting that having such a network in place could have provided meaningful information for the hazard assessment. The small number of sensors required (three spanning nearly 700 km of the Northern Chile coast) may be useful in implementing such networks in places where funding of denser arrays is difficult.
Groundwater storage in aquifers has become a vital water source due to water scarcity in recent years. However, aquifer systems are full of uncertainties, which inevitably propagate throughout the modeling computations, mainly reducing the reliability of the model output. This study develops a novel two-dimensional stochastic confined groundwater flow model. The proposed model is developed by linking the stochastic governing partial differential equations by means of their one-to-one correspondence to the nonlocal Lagrangian-Eulerian extension to the Fokker-Planck equation (LEFPE). In the form of the LEFPE, the resulting deterministic governing equation describes the spatio-temporal evolution of the probability density function of the state variables in the confined groundwater flow process by one single numerical realization instead of requiring thousands of simulations in the Monte Carlo approach. Consequently, the ensemble groundwater flow process's mean and standard deviation behavior can be modeled under uncertainty in the transmissivity field and recharge and/or pumping conditions. In addition, an appropriate numerical method for LEFPE's solution is subsequently devised. Then, its solution is presented, discussed, and illustrated through a numerical example, which is compared against the results obtained by means of the Monte Carlo simulations. Results suggest that the proposed model appropriately characterizes the ensemble behavior in confined groundwater systems under uncertainty in the transmissivity field.
A description of the inversion algorithm used to compute the initial ocean surface deformation from sensor-obtained time series should be provided. A great deal of the error and uncertainties the authors are trying to quantify have as much to do with limitations in the inversion scheme as with location of the sensors. Some of the observed errors may have little to do with sensor location, but a lot with other inversion parameters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.