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We are currently developing a stochastic model for bioremediation that will allow us to estimate the reduction of risk as a function of remediation time. After the current project year, we will have developed the framework of the stochastic bioremediation model, obtained information on the variability of bioremediation at sites, and begun development of stochastic descriptions of model parameters.

Continuation of this project will allow incorporation of the bioremediation model with the risk assessment model and application of the model to evaluate risk reduction at sites. This will provide a tool for stochastically evaluating how risks at a site change as bioremediation is applied and thereby provide a means for determining how long the treatment period must last.

The project goal will be accomplished by completing five specific objectives. The first two will be completed and the third will commence during the current project year.

The third, fourth, and fifth specific objectives will be completed during the proposed continuation project period. Specific Objective 1 is to develop a framework for the stochastic bioremediation model. Two approaches for this model have been identified. A first-order decay model will provide a simple framework that can be used with little data from the site. A more complex model based on the Haldane kinetics will provide the framework for the model when sufficient data are available to characterize its additional parameters.

The second objective will be to obtain data on the variability of bioremediation. Two types of variability will be considered -- that resulting from the natural variability of the site and that resulting from interactions among the site characteristics and the processes of bioremediation. Information on the first type will be quantified by evaluating variability of treatability data for bioremediation. An example of the second type would be the effect of the variability of hydraulic conductivity in an aquifer on the ability to supply microorganisms with oxygen and nutrients.

Scenarios for bioremediation in an aquifer and in a pond will be studied in quantifying such sources of variability. Hand, foot, and mouth disease HFMD is a global infectious disease resulting in millions of cases and even hundreds of deaths.

Therefore, establishing a sensitive and accurate early warning Operation of reservoirs is a fundamental issue in water resource management. We herein investigate well-posedness of an optimal control problem for irrigation water intake from a reservoir in an irrigation scheme, the water dynamics of which is modeled with stochastic differential equations.

A prototype irrigation scheme is being developed in an arid region to harvest flash In each of them were We develop a doubly stochastic point process model with exponentially decaying pulses to describe the statistical properties of the rainfall intensity process. Mathematical formulation of the point process model is described along with second-order moment characteristics of the rainfall depth and aggregated processes. The derived second-order properties of the accumulated Understanding hydrological processes at catchment scale through the use of hydrological model parameters is essential for enhancing water resource management.

Given the difficulty of using lump parameters to calibrate distributed catchment hydrological models in spatially heterogeneous catchments, a multiple calibration technique was adopted to enhance model calibration in this Delineation of flood risk hotspots can be considered as one of the first steps in an integrated methodology for urban flood risk management and mitigation.

This paper presents a step-by-step methodology in a GIS-based framework for identifying flooding risk hotspots for residential buildings. This is done by overlaying a map of potentially flood-prone areas [estimated through the Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging.

A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and The use of historical data can significantly reduce the uncertainty around estimates of the magnitude of rare events obtained with extreme value statistical models.

For historical data to be included in the statistical analysis a number of their properties, e. Another key aspect of the Coastal flood risk will likely increase in the future due to urban development, sea-level rise, and potential change of storm surge climatology, but the latter has seldom been considered in flood risk analysis.

We propose an integrated dynamic risk analysis for flooding task iDraft framework to assess coastal flood risk at regional scales, considering integrated dynamic effects The risk from natural catastrophes is typically estimated using complex simulation models involving multiple stochastic components in a nested structure.

This risk is principally assessed via the mean annual loss, and selected quantiles of the annual loss. Determining an appropriate simulation strategy is important in order to achieve satisfactory convergence of these statistics Understanding the spread of any disease is a highly complex and interdisciplinary exercise as biological, social, geographic, economic, and medical factors may shape the way a disease moves through a population and options for its eventual control or eradication. Disease spread poses a serious threat in animal and plant health and has implications for ecosystem functioning and Multi-regression, hydrologic sensitivity and hydrologic model simulations were applied to quantify the climate change and anthropogenic intervention impacts on the Lower Zab River basin LZRB.

The Pettitt, precipitation-runoff double cumulative curve PR-DCC and Mann—Kendall methods were used for the change points and significant trend analyses in the annual streamflow.

Natural hazards have the potential to trigger complex chains of events in technological installations leading to disastrous effects for the surrounding population and environment. The threat of climate change of worsening extreme weather events exacerbates the need for new models and novel methodologies able to capture the complexity of the natural-technological interaction in Accounting for the stochastic nature of environmental outcomes when quantifying economic environmental trade-offs with mathematical programming models requires the use of probabilistic programming approaches like the upper partial moment UPM method.

Application of the UPM model may result in overregulation and losses in farm profit because the probabilistic constraint is The air pollution concentration data were obtained from the air-quality monitoring system of the city in — In the last two decades, two important avian influenza viruses infecting humans emerged in China, the highly pathogenic avian influenza HPAI H5N1 virus in the late nineties, and the low pathogenic avian influenza LPAI H7N9 virus in China is home to the largest population of chickens 4.

Disease outbreaks are often accompanied by a wealth of data, usually in the form of movements, locations and tests. This data is a valuable resource in which data scientists and epidemiologists can reconstruct the transmission pathways and parameters and thus devise control strategies. However, the spatiotemporal data gathered can be both vast whilst at the same time incomplete This study conducts coupled simulation of strong motion and tsunami using stochastically generated earthquake source models.

It is focused upon the Tohoku, Japan earthquake. The ground motion time-histories are simulated using the multiple-event stochastic finite-fault method, which takes into account multiple local rupture processes in strong motion generation areas.

Flood mitigation should deal with those most sensitive flooding elements to very efficiently release risks and reduce losses.

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