Re-appraisal of earthquake catalog in the Pamir―Hindu Kush region,emphasizing the early and modern instrumental earthquake events

Journal of Seismology - The Pamir-Hindu Kush region is seismically the most dynamic and active zone that went through many devastating earthquakes. While much research is ongoing to produce seismic...     

Numerical modelling of the effect of horizontal drains in centrifuge tests on soil-structure interaction in liquefiable soils

Bulletin of Earthquake Engineering - The research presented herein was carried out in the framework of the H2020 LIQUEFACT project ( http://www.LIQUEFACT.eu/ ). This paper presents the results of a...     

Comparison of Volterra Model and Artificial Neural Networks for Rainfall–Runoff Simulation

This study evaluates the performances of two distinct linear and non-linear models for simulating non-linear rainfall–runoff processes and their applications to flood forecasting in the Navrood River basin, Iran. Due to the excellent capacity of the artificial neural networks [multilayer perceptron (MLP)] and Volterra model, these models were used to approximate arbitrary non-linear rainfall–runoff processes. The MLP model was trained using two different training algorithms. The Volterra model was applied as a linear model [the first-order Volterra (FOV) model] and solved using the traditional ordinary least-square (OLS) method. Storm events within the Navrood River basin were used to verify the suitability of the two models. The models’ performances were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge, and error of time for peak to arrive. Results indicated that the non-linear MLP models outperform the linear FOV model. The latter was ineffective because of the non-linearity of the rainfall–runoff process. Moreover, the OLS method is inefficient when the FOV model has many parameters that must be estimated.     

A GIS-based earthquake damage assessment and settlement methodology

Earthquakes have a greater effect on society than most people think. These effects range from structural damages to economic impacts and fatalities. An earthquake only lasts for a few seconds and the aftershocks may continue for days, but the damage does continue for years. Residential site safety and earthquake damage assessment studies play a crucial role in developing reliable rehabilitation and development programs, improving preparedness and mitigating losses in urbanized areas. The extremely densely populated metropolis of Tehran, which totals of 7,768,561 for 22 districts (according to the 2006 population census), coupled with the fragility of houses and infrastructure, highlight the necessity of a reliable earthquake damage assessment based on essential datasets, such as building resistance attributes, building population, soil structures, streets network and hazardous facilities. This paper presents a GIS-based model for earthquake loss estimation for a district in Tehran, Iran. Damages to buildings were calculated only for the ground shaking effect of one of the region's most active faults, the Mosha Fault in a likely earthquake scenario. Earthquake intensity for each building location was estimated based on attenuation relation and the ratio of damage was obtained from customized fragility curves. Human casualties and street blockages caused by collapsed buildings were taken into account in this study, as well. Finally, accessibility verification found locations without clear passages for temporary settlements by buildings via open streets. The model was validated using the 2003 Bam earthquake damages. The proposed model enables the decision-makers to make more reliable decisions based on various spatial datasets before and after an earthquake occurs. The results of the earthquake application showed total losses as follows: structural damages reaching 64% of the building stock, a death rate of 33% of all the residents, a severe injury rate reaching 27% of the population and street closures upwards of 22% due to building collapse.     

Accuracy evaluation of ClimGen weather generator and daily to hourly disaggregation methods in tropical conditions

Daily and sub-daily weather data are often required for hydrological and environmental modeling. Various weather generator programs have been used to generate synthetic climate data where observed climate data are limited. In this study, a weather data generator, ClimGen, was evaluated for generating information on daily precipitation, temperature, and wind speed at four tropical watersheds located in Hawai??i, USA. We also evaluated different daily to sub-daily weather data disaggregation methods for precipitation, air temperature, dew point temperature, and wind speed at M??kaha watershed. The hydrologic significance values of the different disaggregation methods were evaluated using Distributed Hydrology Soil Vegetation Model. MuDRain and diurnal method performed well over uniform distribution in disaggregating daily precipitation. However, the diurnal method is more consistent if accurate estimates of hourly precipitation intensities are desired. All of the air temperature disaggregation methods performed reasonably well, but goodness-of-fit statistics were slightly better for sine curve model with 2?h lag. Cosine model performed better than random model in disaggregating daily wind speed. The largest differences in annual water balance were related to wind speed followed by precipitation and dew point temperature. Simulated hourly streamflow, evapotranspiration, and groundwater recharge were less sensitive to the method of disaggregating daily air temperature. ClimGen performed well in generating the minimum and maximum temperature and wind speed. However, for precipitation, it clearly underestimated the number of extreme rainfall events with an intensity of >100 mm/day in all four locations. ClimGen was unable to replicate the distribution of observed precipitation at three locations (Honolulu, Kahului, and Hilo). ClimGen was able to reproduce the distributions of observed minimum temperature at Kahului and wind speed at Kahului and Hilo. Although the weather data generation and disaggregation methods were concentrated in a few Hawaiian watersheds, the results presented can be used to similar mountainous location settings, as well as any specific locations aimed at furthering the site-specific performance evaluation of these tested models.     

Field Survey of the March 28, 2005 Nias-Simeulue Earthquake and Tsunami

On the evening of March 28, 2005 at 11:09?p.m. local time (16:09 UTC), a large earthquake occurred offshore of West Sumatra, Indonesia. With a moment magnitude (M _w) of 8.6, the event caused substantial shaking damage and land level changes between Simeulue Island in the north and the Batu Islands in the south. The earthquake also generated a tsunami, which was observed throughout the source region as well as on distant tide gauges. While the tsunami was not as extreme as the tsunami of December 26th, 2004, it did cause significant flooding and damage at some locations. The spatial and temporal proximity of the two events led to a unique set of observational data from the earthquake and tsunami as well as insights relevant to tsunami hazard planning and education efforts.     

Curvilinear immersed boundary method for simulating coupled flow and bed morphodynamic interactions due to sediment transport phenomena

The fluid-structure interaction curvilinear immersed boundary (FSI-CURVIB) numerical method of Borazjani et al. [3] is extended to simulate coupled flow and sediment transport phenomena in turbulent open-channel flows. The mobile channel bed is discretized with an unstructured triangular mesh and is treated as a sharp-interface immersed boundary embedded in a background curvilinear mesh used to discretize the general channel outline. The unsteady Reynolds-averaged Navier-Stokes (URANS) equations closed with the k − ω turbulence model are solved numerically on a hybrid staggered/non-staggered grid using a second-order accurate fractional step method. The bed deformation is calculated by solving the sediment continuity equation in the bed-load layer using an unstructured, finite-volume formulation that is consistent with the CURVIB framework. Both the first-order upwind and the higher-order hybrid GAMMA schemes [12] are implemented to discretize the bed-load flux gradients and their relative accuracy is evaluated through a systematic grid refinement study. The GAMMA scheme is employed in conjunction with a sand-slide algorithm for limiting the bed slope at locations where the material angle of repose condition is violated. The flow and bed deformation equations are coupled using the partitioned loose-coupling FSI-CURVIB approach [3]. The hydrodynamic module of the method is validated by applying it to simulate the flow in an 180° open-channel bend with fixed bed. To demonstrate the ability of the model to simulate bed morphodynamics and evaluate its accuracy, we apply it to calculate turbulent flow through two mobile-bed open channels, with 90° and 135° bends, respectively, for which experimental measurements are available.     

Selection of the best fit flood frequency distribution and parameter estimation procedure: a case study for Tasmania in Australia

Selection of a flood frequency distribution and associated parameter estimation procedure is an important step in flood frequency analysis. This is however a difficult task due to problems in selecting the best fit distribution from a large number of candidate distributions and parameter estimation procedures available in the literature. This paper presents a case study with flood data from Tasmania in Australia, which examines four model selection criteria: Akaike Information Criterion (AIC), Akaike Information Criterion—second order variant (AIC_c), Bayesian Information Criterion (BIC) and a modified Anderson–Darling Criterion (ADC). It has been found from the Monte Carlo simulation that ADC is more successful in recognizing the parent distribution correctly than the AIC and BIC when the parent is a three-parameter distribution. On the other hand, AIC and BIC are better in recognizing the parent distribution correctly when the parent is a two-parameter distribution. From the seven different probability distributions examined for Tasmania, it has been found that two-parameter distributions are preferable to three-parameter ones for Tasmania, with Log Normal appears to be the best selection. The paper also evaluates three most widely used parameter estimation procedures for the Log Normal distribution: method of moments (MOM), method of maximum likelihood (MLE) and Bayesian Markov Chain Monte Carlo method (BAY). It has been found that the BAY procedure provides better parameter estimates for the Log Normal distribution, which results in flood quantile estimates with smaller bias and standard error as compared to the MOM and MLE. The findings from this study would be useful in flood frequency analyses in other Australian states and other countries in particular, when selecting an appropriate probability distribution from a number of alternatives.