Abstract: Many scientific works have focused on developing propagation models that predict forest fires behavior. These models require a precise knowledge of the environment where the fire is taking place. Geographical Information Systems allow us determining and building the different information layers that define the terrain and the fire. These data, along with meteorological information from weather services, enables the simulation based on real conditions. However, fire spread prediction models require a set of input parameters that, in some cases, are difficult to know or even estimate precisely. Therefore, a framework, based on a genetic algorithm calibration stage, was introduced to reduce the uncertainty in the input parameters and improve the accuracy of the predictions. This stage is implemented using a MPI master/worker scheme and an OpenMP parallel version of the fire spread simulator. Additionally, the whole system is run using suitable automatic worker-assignment and core-allocation policies to respect the existing time restrictions, inherent to this real-world problem. This paper details the process of obtaining the necessary input data as well as the parallel evolutionary framework that delivers the final prediction. A real case study is presented to illustrate the way this framework works.

Abstract: Hydraulic fracturing is a procedure of injecting high pressure fluid into the wellbore in order to break shell rock and facilitate gas flow. It is a very costly procedure and, if not conducted properly, it may lead to environment contamination. To avoid costs associated with pumping fluid outside the perspective (gas rich) zone and improve one’s knowledge about the reservoir rock, microseismic monitoring can be applied. The method involves recording seismic waves, which are induced by fractured rock, by an array of sensors distributed in a wellbore nearby or on the surface. Combining geological and geophysical knowledge of region with signal processing technics, one can locate induced fractures allowing for real-time process monitoring and rock properties evaluation. In Poland perspective shell formation is located very deep -about 4km from the surface. Additionally overlaying rock formations strongly attenuate and disperse seismic waves. Therefor signal recorded by a surface array of sensors is very weak. Signal from a seismic event can be orders of magnitude lower than noise. To recover signal connected with fractured rock one needs to use methods utilizing coherence of signals. An example of such analysis procedure is presented.

Abstract: This study introduces a web-based visual analytic framework to better understand the software structures of large-scale environmental models. The framework integrates data management, software structures analysis, and web-based visualizations. A system for the Community Land Model (CLM) is developed to demonstrate the capability of the proposed framework. It consists of three major components: (1) a Fortran-syntax analysis tool that decomposes CLM source code into simpler forms; (2) an application tier that further analyzes and converts the preprocessed data into meaningful software structural information; (3) a web-based front end that is developed using state-of-the-art web technologies and visualization toolkit (e.g., D3.js). The framework provides users with easy access to the internal structures of complex environmental models. Currently, the prototype system is being used by CLM modelers and field scientists to tackle different environmental research problems.

Abstract: The authors propose a three-tier framework model that can be used to conceptualise and study the overall economic and ecological impact of large-scale applied environmental modelling services. While some of these services (such as weather forecast) have clear, near-universally accepted positive net impact on society, there are many emerging candidate services whose sustainability requires systematic cost-benefit analysis. We think that the proposed model should facilitate both performing this analysis, and verifying and communicating the results e.g. to funding agencies or general public. The tiers we propose consist of: 1. Economical and ecological operational costs (OPEX) that are directly related to the execution of the modelling steps (computing and data processing). These include power consumption, salaries and office costs of the IT service staff – and the associated environmental impacts. 2. The economic and environmental capital expenditures (CAPEX) that consists of IT infrastructure investments (either direct, on-premises ones or ones at Cloud service providers’ presemises), investments in buildings and other infrastructure, immaterial costs (e.g. software development or license fees) and so on. 3. The environmental impact of the real-world application of the new modelling services. The impact assessment should consider both the direct impact (reduced risks, optimised logistics) as well as the possible indirect impacts (increased volume of shipments due to reduced costs and risks offsetting per-shipment optimisation). We will present some examples of how to link these analysis components together in specific use cases, ranging from disaster risk reduction to international logistics. We will also discuss the challenges related to timescales involved: for example, OPEX represents an immediate cost, whereas the third-tier impact of disaster preparedness for a natural hazard scenario with very long return period (“100 year earthquake”) introduces considerable uncertainties to short-term cost-benefit calculations.