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In this work an analysis of the historical monthly records of the rainfall measurement network for Venezuela is presented in terms of the quantity of available stations and quality of the data records. Therefore, an analysis of 1864 precipitation stations of Venezuela, coming from the database arising from the project \emph{Development of a Hydroclimatic Data Repository for Epidemiological and Environmental Risk Assessments}, was developed. The main quality criteria analyzed were the following: the proportion of missing and aggregated data, and the weight each type of data has upon the total record; the longevity of stations and the percentage of functioning stations during the whole period of records, which provides a temporal analysis of the historical functioning of the network. The degree of record intermittency is inspected by calculating the percentage of consecutive periods for which data is available. This analysis is presented for each state in the country, with the purposed of providing to researchers and users of climatic data, objective information to evaluate the evolution of the measurement network in time and the data quality conditions of the available information for each Venezuelan state.
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... The x axis corresponds to the longitude, and the y axis to the latitude, both expressed in degrees. Left image source isSajo-Castelli et al. (2014) ...
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Extreme environmental events have considerable impacts on society. Preparation to mitigate or forecast accurately these events is a growing concern for governments. In this regard, policy and decision makers require accurate tools for risk estimation in order to take informed decisions. This work proposes a Bayesian framework for a unified treatment and statistical modeling of the main components of risk: hazard, vulnerability and exposure. Risk is defined as the expected economic loss or population affected as a consequence of a hazard event. The vulnerability is interpreted as the loss experienced by an exposed population due to hazard events. The framework combines data of different spatial and temporal supports. It produces a sequence of temporal risk maps for the domain of interest including a measure of uncertainty for the hazard and vulnerability. In particular, the considered hazard (rainfall) is interpolated from point-based measured rainfall data using a hierarchical spatio-temporal Kriging model, whose parameters are estimated using the Bayesian paradigm. Vulnerability is modeled using zero-inflated distributions with parameters dependent on climatic variables at local and large scales. Exposure is defined as the total population settled in the spatial domain and is interpolated using census data. The proposed methodology was applied to the Vargas state of Venezuela to map the spatio-temporal risk for the period 1970–2006. The framework highlights both high and low risk areas given extreme rainfall events.
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The global hydrological cycle is a key component of Earth's climate system. A significant amount of the energy the Earth receives from the Sun is redistributed around the world by the hydrological cycle in the form of latent heat flux. Changes in the hydrological cycle have a direct impact on droughts, floods, water resources and ecosystem services. Observed land precipitation and global river discharges do not show an increasing trend as might be expected in a warming world. Here we show that this apparent discrepancy can be resolved when the effects of tropospheric aerosols are considered. Analysing state-of-the-art climate model simulations, we find for the first time that there was a detectable weakening of the hydrological cycle between the 1950s and the 1980s, attributable to increased anthropogenic aerosols, after which the hydrological cycle recovered as a result of increasing greenhouse gas concentrations. The net result of these two counter-acting effects is an insignificant trend in the global hydrological cycle, but the individual influence of each is substantial. Reductions in air pollution have already shown an intensification in the past two decades and a further rapid increase in precipitation could be expected if the current trend continues.
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The precipitation is one of the most important atmospheric processes used to the characterisation of environmental and climatic conditions everywhere on the earth surface. Due to the complexity associated to its occurrence, measurement and estimation, the study of its distribution and variability require to be permanently reviewed and evaluated. The measured or estimated values of precipitation for each place or area, and their variability, are parameters of extraordinary importance for a considerable number of objects, including research, planning, land use planning, risk prevention. This research aims to build distribution models of precipitation to temporal and spatial levels for the state of Tachira, using GIS¿s tools and geostatistic techniques. By applying GIS¿s software such as MapInfo version 7.0 and ArcView version 3.1., the precipitation database and cartography of the state were processed. In order to carry out a statistical analysis a Kriging Interpolator Module was used. The Interpolator allowed obtaining estimations and modelling of the temporal and spatial distribution of precipitation mean for the state, and the correspondent estimation errors. These models of isohyets allow to have a cartography base in digital format which can be used to study of distribution of precipitation patterns, also the capacity to estimate, with the minimal error as possible, the precipitation mean for any place in the state of Tachira
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En el presente trabajo se propone un método de reconstrucción de series de tiempo de precipitaciones, para ser aplicado a las estaciones meteorológicas de Venezuela con el propósito de corregir el problema de datos faltantes. La metodología se fundamenta en dos técnicas: la primera reconstruye la dinámica y el tiempo de retardo del sistema dinámico de la serie temporal, y la segunda utiliza un modelo de redes neuronales para predecir los datos faltantes. Los modelos de redes neuronales exploran la dependencia espacio temporal de los atributos meteorológicos de las series y constituyen una herramienta importante para la propagación de la información relacionada con el clima, y además proveen soluciones prácticas de incertidumbre asociados con la interpolación y la captura de la estructura espacio temporal de los datos. Para llevar a cabo estos procedimientos, se ha determinado la dimensión de inmersión del atractor de las series y el tiempo de retardo, y luego se han usado estas medidas para definir la arquitectura de la red neuronal. El algoritmo utilizado para estimar los parámetros de la red neuronal ha sido el de retropropagación, que básicamente actualiza los pesos del modelo en la dirección en que el gradiente decrece más rápidamente. Para seleccionar la arquitectura de la red, se ha usado el criterio de información de Bayes (BIC), que consiste en penalizar el error cuadrático medio de los parámetros utilizados en el ajuste del modelo. Los resultados indican que las series de precipitaciones en Venezuela tienen alguna estructura subyacente no lineal, y provienen de un sistema caótico de bajas dimensiones. Los modelos de redes neuronales se han revelado útiles para la reconstrucción de los datos faltantes de las series.
 The need for high resolution rainfall data at temporal scales varying from daily to hourly or even minutes is a very important problem in hydrology. For many locations of the world, rainfall data quality is very poor and reliable measurements are only available at a coarse time resolution such as monthly. The purpose of this work is to apply a stochastic disaggregation method of monthly to daily precipitation in two steps: 1. Initialization of the daily rainfall series by using the truncated normal model as a reference distribution. 2.␣Restructuring of the series according to various time series statistics (autocorrelation function, scaling properties, seasonality) by using a Markov chain Monte Carlo based algorithm. The method was applied to a data set from a rainfall network of the central plains of Venezuela, in where rainfall is highly seasonal and data availability at a daily time scale or even higher temporal resolution is very limited. A detailed analysis was carried out to study the seasonal and spatial variability of many properties of the daily rainfall as scaling properties and autocorrelation function in order to incorporate the selected statistics and their annual cycle into an objective function to be minimized in the simulation procedure. Comparisons between the observed and simulated data suggest the adequacy of the technique in providing rainfall sequences with consistent statistical properties at a daily time scale given the monthly totals. The methodology, although highly computationally intensive, needs a moderate number of statistical properties of the daily rainfall. Regionalization of these statistical properties is an important next step for the application of this technique to regions in where daily data is not available.
4: Longevidad de las estaciones (en unidades anuales) para el estado Zulia
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Figura ZU.4: Longevidad de las estaciones (en unidades anuales) para el estado Zulia. 1064
4: Continuación. Longevidad de las estaciones (en unidades anuales) para el estado Zulia
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Figura ZU.4: Continuación. Longevidad de las estaciones (en unidades anuales) para el estado Zulia. 1136 2048 2046 2049 2047 2045
8: Segregación de las estaciones según tipo de dato registrado
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Figura MO.8: Segregación de las estaciones según tipo de dato registrado.
Lista de estaciones para el estado Monagas
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Tabla MO.9: Lista de estaciones para el estado Monagas.
HIDROX. Repositorio de Datos Hidroclimáticos para la Gestión de Riesgos Epidemiológicos y Ambientales
  • L Bravo
  • S Abad
  • I Llatas
  • A Salcedo
  • L Delgado
  • S Ramos
  • K Cordova
Bravo, L., Abad, S., Llatas, I., Salcedo, A., Delgado, L., Ramos, S., y Cordova, K. (2014). HIDROX. Repositorio de Datos Hidroclimáticos para la Gestión de Riesgos Epidemiológicos y Ambientales. USB-CESMA, 1 edición. 80 pp.
P5. Proyecciones de población por año, según sexo y municipio
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INE (2014). P5. Proyecciones de población por año, según sexo y municipio, 2011-14 (Síntesis Estadística Estadal). Instituto Nacional de Estadística.