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Abstract. This investigation addresses the tsunami inundation in Lima and Callao caused by the massive 1746 earthquake (Mw 9.0) along the Peruvian coast. Numerical modeling of the tsunami inundation processes in the nearshore includes strong nonlinear numerical terms. In a comparative analysis of the calculation of the tsunami wave effect, two numerical codes are used, Tsunami-HySEA and TsunAWI, which both solve the shallow water (SW) equations but with different spatial approximations. The comparison primarily evaluates the flow velocity fields in inundated areas. The relative importance of the various parts of the SW equations is determined, focusing on the nonlinear terms. Particular attention is paid to the contribution of momentum advection, bottom friction, and volume conservation. The influence of the nonlinearity on the degree and volume of inundation, flow velocity, and small-scale fluctuations is determined. The sensitivity of the solution concerning the bottom friction parameter is also investigated, showing the effects of nonlinearity processes in the inundated areas, wave heights, current velocity, and the spatial structure variations shown in tsunami inundation maps.

Abstract. In this paper, we anticipate geospatial population distributions to quantify the future number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru. We capitalize upon existing gridded population time series data sets, which are provided on an open-source basis globally, and implement machine learning models tailored for time series analysis, i.e., based on long short-term memory (LSTM) networks, for prediction of future time steps. Specifically, we harvest WorldPop population data and teach LSTM and convolutional LSTM models equipped with both unidirectional and bidirectional learning mechanisms, which are derived from different feature sets, i.e., driving factors. To gain insights regarding the competitive performance of LSTM-based models in this application context, we also implement multilinear regression and random forest models for comparison. The results clearly underline the value of the LSTM-based models for forecasting gridded population data; the most accurate prediction obtained with an LSTM equipped with a bidirectional learning scheme features a root-mean-squared error of 3.63 people per 100 × 100 m grid cell while maintaining an excellent model fit (R2= 0.995). We deploy this model for anticipation of population along a 3-year interval until the year 2035. Especially in areas of high peak ground acceleration of 207–210 cm s−2, the population is anticipated to experience growth of almost 30 % over the forecasted time span, which simultaneously corresponds to 70 % of the predicted additional inhabitants of Lima. The population in the tsunami inundation area is anticipated to grow by 61 % until 2035, which is substantially more than the average growth of 35 % for the city. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.

Abstract. We propose the use of variable resolution boundaries
based on central Voronoi tessellations (CVTs) to spatially aggregate building
exposure models for risk assessment to various natural hazards. Such a
framework is especially beneficial when the spatial distribution of the
considered hazards presents intensity measures with contrasting footprints
and spatial correlations, such as in coastal environments. This work avoids
the incorrect assumption that a single intensity value from hazards with low
spatial correlation (e.g. tsunami) can be considered to be representative
within large-sized geo-cells for physical vulnerability assessment, without,
at the same time, increasing the complexity of the overall model. We present
decoupled earthquake and tsunami scenario-based risk estimates for the
residential building stock of Lima (Peru). We observe that earthquake loss
models for far-field subduction sources are practically insensitive to the
exposure resolution. Conversely, tsunami loss models and associated
uncertainties depend on the spatial correlations of the hazard intensities
as well as on the resolution of the exposure models. We note that for the
portfolio located in the coastal area exposed to both perils in Lima, the
ground shaking dominates the losses for lower-magnitude earthquakes, whilst
tsunamis cause the most damage for larger-magnitude events. For the latter,
two sets of existing empirical flow depth fragility models are used,
resulting in large differences in the calculated losses. This study,
therefore, raises awareness about the uncertainties associated with the
selection of fragility models and spatial aggregation entities for exposure
modelling and loss mapping.