Development,urban planning and political decisions. A triad that built territories at risk

Natural Hazards - Chilean geography exposes the country to high-level risks such as earthquakes and tsunamis. The disasters of 1930, 1960, 2010, and 2014 testify to the continuous link between...     

Integrated and interdisciplinary scientific approach to coastal management

Coastal zones and beach management practices, regulatory decisions, and land use planning activities along coastal zones have historically been made with insufficient information concerning the dynamic coastal environment. In this study we address and integrate an interdisciplinary scientific approach to Coastal Management in a scenario where lack of this information has resulted in the alteration of the natural dune system of the beach of Cala Millor (Mallorca, Balearic Islands, Spain), and also in the perception of the beach retreat and in a parallel way, a risk for the tourism resources. In this work the detailed studies on beach morphodynamics have been developed as a basis for integrating proper beach management, beach natural dynamics and local users and economic agent interests. From this point of view a set of solutions are considered as the basis for a management policy that links beach science and beach use as a tourism resort resource.     

Anthropogenic subsidence in the Mexicali Valley,Baja California,Mexico, and slip on the Saltillo fault

Deep fluid extraction in the Cerro Prieto geothermal field (CPGF) has caused subsidence and induced slip on tectonic faults in the Mexicali Valley (Baja California, Mexico). The Mexicali Valley is located in the southern part of the Salton Trough, at the boundary between the Pacific and North American plates. The Valley is characterized by being a zone of continuous tectonic deformation, geothermal activity, and seismicity. Within the Cerro Prieto pull-apart basin, seismicity is concentrated mainly in swarms, while strong earthquakes have occurred in the Imperial and Cerro Prieto transform faults, that are the eastern and western bound of the basin. Since 1973, fluid extraction at the CPGF has influenced deformation in the area, accelerating the subsidence and causing rupture (frequently as vertical slip or creep) on the surface traces of tectonic faults. Both subsidence and fault slip are causing damage to infrastructure like roads, railroad tracks, irrigation channels, and agricultural fields. Currently, accelerated extraction in the eastern part of CPGF has shifted eastwards the area of most pronounced subsidence rate; this accelerated subsidence can be observed at the Saltillo fault, a southern branch of the Imperial fault in the Mexicali Valley. Published leveling data, together with field data from geological surveys, geotechnical instruments, and new InSAR images were used to model the observed deformation in the area in terms of fluid extraction. Since the electricity production in the CPGF is an indispensable part of Baja California economy, extraction is sure to continue and may probably increase, so that the problem of damages caused by subsidence will likely increase in the future.     

Assessment of possible peak accelerations through stochastic variations

A method is presented for the estimation of possible maximum accelerations, as well as of other accelerogram or seismogram characteristics, based on the stochastic variation of the spectral phase of pulses from an observed time series of interest, possibly the ‘design earthquake’. For a seismic source with the same spectral amplitude the variations are caused by slight differences in source time function, source or receiver location, etc. A large number of variations define the range of possible time series, as well as clear and stable relationships between the standard deviations of phase variation distributions (here considered Gaussian) and possible values of the studied features, as well as their distributions and probabilities.     

Potential for small scale added value of RCM’s downscaled climate change signal

In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution downscaling generates fine-scale details in the climate projection derived using the “delta method”. Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the “potential” of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60 % of the total CC signal in some coastal regions although for most regions the fine scale contributions to the total CC signal are of around ～5 %. For the precipitation variable, fine scales contribute to a change of generally less than 15 % of the seasonal-averaged precipitation in present climate with a continental North American average of ～5 % in both summer and winter seasons. In the case of precipitation, uncertainty due to sampling issues may further dilute the information present in the downscaled fine scales. These results suggest that users of RCM simulations for climate change studies in a delta method framework have little high-resolution information to gain from RCMs at least if they limit themselves to the study of first-order statistical moments. Other possible benefits arising from the use of RCMs—such as in the large scale of the downscaled fields– were not explored in this research.     

Nonparametric analysis of high wind speed data

In this paper, nonparametric curve estimation methods are applied to analyze time series of wind speeds, focusing on the extreme events exceeding a chosen threshold. Classical parametric statistical approaches in this context consist in fitting a generalized Pareto distribution (GPD) to the tail of the empirical cumulative distribution, using maximum likelihood or the method of the moments to estimate the parameters of this distribution. Additionally, confidence intervals are usually computed to assess the uncertainty of the estimates. Nonparametric methods to estimate directly some quantities of interest, such as the probability of exceedance, the quantiles or return levels, or the return periods, are proposed. Moreover, bootstrap techniques are used to develop pointwise and simultaneous confidence intervals for these functions. The proposed models are applied to wind speed data in the Gulf Coast of US, comparing the results with those using the GPD approach, by means of a split-sample test. Results show that nonparametric methods are competitive with respect to the standard GPD approximations. The study is completed generating synthetic data sets and comparing the behavior of the parametric and the nonparametric estimates in this framework.     

Automatic seismic wave detection and autoregressive model method

The problem of automatic detection of seismic waves by large telemetered seismic networks such as the Mexican Continental Aperture Seismic Network (RESMAC), is extended here to include determination of seismic first-arrival and S-phase-arrival times. A short general outline of the detection problem background and a small introduction to the autoregressive model (AR) concept are presented. Several automatic detection algorithms were implemented and compared with a newly developed autoregressive algorithm. Careful consideration of the advantages and disadvantages of each method determined that a mixed detection scheme is optimal and suitable for RESMAC. A few examples are shown that illustrate the relative performances of the methods tried here. The proposed detection scheme has the following characteristics: (a) First-arrival detection, based on a simple (average of squared input) characteristic function, and a trigger criterion that uses as a distortion measure the long-average-to-short-average ratio of the characteristic function, checked using a duration criterion; (b) use of two threshold values, one for triggering, and another for beginning the backward search for the phase arrival time; (c) use of the autoregressive model (AR) method, with the Itakura-Saito distortion measure, for S-phase detection, checked using both duration and amplitude criteria; and (d) characterization of the reliability of the determinations for their subsequent use in automatic location programs, alarms, etc. The automatic detection scheme has proved effective.     

利用遥感技术建立墨西哥热带地上生物量模型(英文)

Spatially-explicit estimation of aboveground biomass(AGB) plays an important role to generate action policies focused in climate change mitigation,since carbon(C) retained in the biomass is vital for regulating Earth’s temperature.This work estimates AGB using both chlorophyll(red,near infrared) and moisture(middle infrared) based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer(MODIS) and MOD44B vegetation continuous fields(VCF) data.The study area is located in San Luis Potosí,Mexico,a region that comprises a part of the upper limit of the intertropical zone.AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature.Linear and nonlinear(expo-nential) models were fitted to find their predictive potential when using satellite spectral data as explanatory variables.Highly-significant correlations(p = 0.01) were found between all the explaining variables tested.NDVI62,linked to chlorophyll content and moisture stress,showed the highest correlation.The best model(nonlinear) showed an index of fit(Pseudo-r2) equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables.Validation correlation coefficients were similar for both models:lin-ear(r = 0.87**) and nonlinear(r = 0.86**).