stochastic environmental research and risk assessment pdf

Stochastic environmental research and risk assessment pdf

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Spatio-temporal stochastic modelling (METMAVI)

Stochastic environmental research risk assessment

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On the probability of extinction of the Haiti cholera epidemic. More than three years after its appearance in Haiti, cholera has already caused more than 8, deaths and , infections and it is feared to become endemic. However, no clear evidence of a stable environmental reservoir of pathogenic Vibrio cholerae, the infective agent of the disease, has emerged so far, suggesting the possibility that the transmission cycle of the disease is being maintained by bacteria freshly shed by infected individuals. Should this be the case, cholera could in principle be eradicated from Haiti. Here, we develop a framework for the estimation of the probability of extinction of the epidemic based on current information on epidemiological dynamics and health-care practice. Cholera spreading is modeled by an individual-based spatially-explicit stochastic model that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks.

Spatio-temporal stochastic modelling (METMAVI)

Stochastic Environmental Research and Risk Assessment SERRA publishes research papers, reviews and technical notes on stochastic probabilistic and statistic approaches to environmental sciences and engineering, including the description and prediction of spatiotemporal natural systems under conditions of uncertainty, risk assessment, interactions of earth and atmospheric environments with people and the ecosystem, and environmental health. Its core aim is to bring together research in various fields of environmental, planetary and health sciences, providing an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of novel stochastic techniques used in different fields to the community of interested researchers. Issue 3, March This special issue aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. Quantification and characterization of uncertainty are two key features of modern science-based predictions.

This paper studies the statistics of the soil moisture condition and its monthly variation for the purpose of evaluating drought vulnerability. A zero-dimensional soil moisture dynamics model with the rainfall forcing by the rectangular pulses Poisson process model are used to simulate the soil moisture time series for three sites in Korea: Seoul, Daegu, and Jeonju. These sites are located in the central, south-eastern, and south-western parts of the Korean Peninsular, respectively. The model parameters are estimated on a monthly basis using hourly rainfall data and monthly potential evaporation rates obtained by the Penmann method. The resulting soil moisture simulations are summarized on a monthly basis. In brief, the conclusions of our study are as follows.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: This paper deals with the exhaustion of a replenishable resource, one of the main topics in environmental research. To examine this problem, we construct a mechanism to estimate the probability of depletion under a very small set of assumptions. The stock of the resource is assumed to evolve accordingly to a marked temporal process generated by the shocks occurring in the environment.


Stochastic Environmental Research and Risk Assessment (SERRA) publishes research papers, reviews and technical notes on stochastic (probabilistic and.


Stochastic environmental research risk assessment

Some authors Haines-Young et al. Chow etal. The objective of spatial analysis methods is the assess-ment of spatial uncertainty Goovaerts, or the evaluationof critical scenarios through risk maps, environmental costsmaps, a.

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