Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters

Demands on the disaster response capacity of the European Union are likely to increase, as the impacts of disasters continue to grow both in size and frequency. This has resulted in intensive research on issues concerning spatially-explicit information and modelling and their multiple sources of uncertainty. Geospatial support is one of the forms of assistance frequently required by emergency response centres along with hazard forecast and event management assessment. Robust modelling of natural hazards requires dynamic simulations under an array of multiple inputs from different sources. Uncertainty is associated with meteorological forecast and calibration of the model parameters. Software uncertainty also derives from the data transformation models (D-TM) needed for predicting hazard behaviour and its consequences. On the other hand, social contributions have recently been recognized as valuable in raw-data collection and mapping efforts traditionally dominated by professional organizations. Here an architecture overview is proposed for adaptive and robust modelling of natural hazards, following the Semantic Array Programming paradigm to also include the distributed array of social contributors called Citizen Sensor in a semantically-enhanced strategy for D-TM modelling. The modelling architecture proposes a multicriteria approach for assessing the array of potential impacts with qualitative rapid assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema and complementing more traditional and accurate a-posteriori assessment. We discuss the computational aspect of environmental risk modelling using array-based parallel paradigms on High Performance Computing (HPC) platforms, in order for the implications of urgency to be introduced into the systems (Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua

On the key role of droughts in the dynamics of summer fires in Mediterranean Europe

Summer fires frequently rage across Mediterranean Europe, often intensified by high temperatures and droughts. According to the state-of-the-art regional fire risk projections, in forthcoming decades climate effects are expected to become stronger and possibly overcome fire prevention efforts. However, significant uncertainties exist and the direct effect of climate change in regulating fuel moisture (e.g. warmer conditions increasing fuel dryness) could be counterbalanced by the indirect effects on fuel structure (e.g. warmer conditions limiting fuel amount), affecting the transition between climate-driven and fuel-limited fire regimes as temperatures increase. Here we analyse and model the impact of coincident drought and antecedent wet conditions (proxy for the climatic factor influencing total fuel and fine fuel structure) on the summer Burned Area (BA) across all eco-regions in Mediterranean Europe. This approach allows BA to be linked to the key drivers of fire in the region. We show a statistically significant relationship between fire and same-summer droughts in most regions, while antecedent climate conditions play a relatively minor role, except in few specific eco-regions. The presented models for individual eco-regions provide insights on the impacts of climate variability on BA, and appear to be promising for developing a seasonal forecast system supporting fire management strategies.We thank the European Forest Fire Information System-EFFIS (http://effis.jrc.ec.europa.eu) of the European Commission Joint Research Centre for the fire data. We acknowledge the SPEI data providers (http://sac.csic. es/spei/database.html). Special thanks to Joaquín Bedia, Esteve Canyameras, Xavier Castro and Andrej Ceglar for helpful discussions on the study. This work was partially funded by the Project of Interest “NextData” of the Italian Ministry for Education, University and Research and by the EU H2020 Project 641762 “ECOPOTENTIAL: Improving Future Ecosystem Benefits through Earth Observations”. Ricardo Trigo was supported by IMDROFLOOD funded by Portuguese FCT (WaterJPI/0004/2014).Peer ReviewedPostprint (published version

Flame filtering and perimeter localization of wildfires using aerial thermal imagery

Airborne thermal infrared (TIR) imaging systems are being increasingly used for wild fire tactical monitoring since they show important advantages over spaceborne platforms and visible sensors while becoming much more affordable and much lighter than multispectral cameras. However, the analysis of aerial TIR images entails a number of difficulties which have thus far prevented monitoring tasks from being totally automated. One of these issues that needs to be addressed is the appearance of flame projections during the geo-correction of off-nadir images. Filtering these flames is essential in order to accurately estimate the geographical location of the fuel burning interface. Therefore, we present a methodology which allows the automatic localisation of the active fire contour free of flame projections. The actively burning area is detected in TIR georeferenced images through a combination of intensity thresholding techniques, morphological processing and active contours. Subsequently, flame projections are filtered out by the temporal frequency analysis of the appropriate contour descriptors. The proposed algorithm was tested on footages acquired during three large-scale field experimental burns. Results suggest this methodology may be suitable to automatise the acquisition of quantitative data about the fire evolution. As future work, a revision of the low-pass filter implemented for the temporal analysis (currently a median filter) was recommended. The availability of up-to-date information about the fire state would improve situational awareness during an emergency response and may be used to calibrate data-driven simulators capable of emitting short-term accurate forecasts of the subsequent fire evolution.Postprint (author's final draft

A review of wildland fire spread modelling, 1990-present 2: Empirical and quasi-empirical models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behaviour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of an empirical or quasi-empirical nature. These models are based solely on the statistical analysis of experimentally obtained data with or without some physical framework for the basis of the relations. Other papers in the series review models of a physical or quasi-physical nature, and mathematical analogues and simulation models. The main relations of empirical models are that of wind speed and fuel moisture content with rate of forward spread. Comparisons are made of the different functional relationships selected by various authors for these variables.Comment: 22 pages + 7 pages references + 2 pages tables + 2 pages figures. Submitted to International Journal of Wildland Fir

Estimating the effects of water-induced shallow landslides on soil erosion

Rainfall induced landslides and soil erosion are part of a complex system of multiple interacting processes, and both are capable of significantly affecting sediment budgets. These sediment mass movements also have the potential to significantly impact on a broad network of ecosystems health, functionality and the services they provide. To support the integrated assessment of these processes it is necessary to develop reliable modelling architectures. This paper proposes a semi-quantitative integrated methodology for a robust assessment of soil erosion rates in data poor regions affected by landslide activity. It combines heuristic, empirical and probabilistic approaches. This proposed methodology is based on the geospatial semantic array programming paradigm and has been implemented on a catchment scale methodology using Geographic Information Systems (GIS) spatial analysis tools and GNU Octave. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. In order to improve computational reproducibility, the geospatial data transformations implemented in ESRI ArcGis are made available in the free software GRASS GIS. The proposed modelling architecture is flexible enough for future transdisciplinary scenario analysis to be more easily designed. In particular, the architecture might contribute as a novel component to simplify future integrated analyses of the potential impact of wildfires or vegetation types and distributions, on sediment transport from water induced landslides and erosion.Comment: 14 pages, 4 figures, 1 table, published in IEEE Earthzine 2014 Vol. 7 Issue 2, 910137+ 2nd quarter theme. Geospatial Semantic Array Programming. Available: http://www.earthzine.org/?p=91013

Forest fire simulator system for emergency resources management support

Europe suffers approximately 65,000 fires every year, which burn, on average, half a million hectares of forest areas [1]. The main direct effect of forest fires is the destruction of the natural landscape and the consequent loss of ecosystem service that have drastic economic impact, but mainly and much more important, fires also result in the loss of human lives every year. Although being forest fires a problem present in all EU members, the most affected areas to this hazards are the southern countries due to their climatological conditions. All affected countries invest lots of resources to minimize fire damages, but many times when dealing with large fires, regional and national disaster management units are lack of efficient and reliable tools to help wildfire analysts. In this work, we describe a process to generate on-line wildfire simulations coupled with the regional weather forecast service (Servei Meteorològic de Catalunya, SMC) and the helicopter company (Helipistas S.L) who provides isochronous perimeters of the fire behaviour in a certain moment of the emergency and how both of this data sources feed the inputs for the simulation process.Europa sufre aproximadamente 65,000 incendios cada año, de media, medio millón de hectáreas forestales[1]. El principal efecto de los fuegos forestales es la destrucción de la superfície natural y como consecuencia la pérdida del ecosistema y el gran impacto económico, pero principamente y de manera mucho más importante el fuego tambien repercute en la pérdida de vidas humanas año tras año. Los fuegos forestales además de ser un problema para los miembros de la UE, se ven repercutidos, especialmente los paises del sur debido a sus condiciones climatológicas. Todos estos paises afectados invierten gran cantidad de recursos para minimizar estos efectos. Generalmente cuando se trata de grandes incendios forestales, las unidades de mando de estos medios de exinción a nivel regional y nacional se ven necesitados de herramientas eficientes y útiles para el análisis de la predicción del comportamiento de estos grandes incendios forestales. En este trabajo, describimos un sistema de predicción de incendios forestales acoplado con el servicio meteorológicos de catalunya (SMC) y la empresa de helicópteros (Helipistas S.L) los cuales proveen de los perímetros del incendio en un instante de tiempo de la emergencia y cómo estas dos fuentes de datos se anexan al proceso de simulación.Europa pateix aproximadament 65,000 incendis cada any, de mitja, cada mig-milió d'hectàrees forestals[1]. El principal efecte dels focs forestals es la destrucció de la superfície natural i com a conseqüència la pèrdua de l'ecosistema i el gran impacte econòmic, però principalment i de manera molt més important el foc, també, repercuteix en la pèrdua de vides humanes any rere any. Els focs forestals a més a més de representar un problema pels països membres de la UE, es veuen afectats els països del Sud degut a les seves condicions climatològiques. Tots aquests països afectats inverteixen grans quantitat de recursos per a minimitzar aquests efectes. Generalment quan es tracta de grans incendis forestals, les unitats de comandament d'aquests medis d'extinció a nivell regional i nacional es veuen necessitats d'eines útils i eficients per a l'anàlisis de la predicció en el comportament dels grans incendis forestals. En aquest treball, descrivim un sistema de predicció d'incendis forestals acoblat amb el servei meteorològic de Catalunya (SMC) i l'empresa d'helicòpters (Helipistas S.L) els quals proveïxen dels perímetres de l'incendi en un instant de temps de l'emergència i com aquestes dos fonts de dades annexen al procés de simulació

Burned area prediction with semiparametric models

[Abstract] Wildfires are one of the main causes of forest destruction, especially in Galicia (north-west Spain), where the area burned by forest fires in spring and summer is quite high. This work uses two semiparametric time-series models to describe and predict the weekly burned area in a year: autoregressive moving average (ARMA) modelling after smoothing, and smoothing after ARMA modelling. These models can be described as a sum of a parametric component modelled by an autoregressive moving average process and a non-parametric one. To estimate the non-parametric component, local linear and kernel regression, B-splines and P-splines were considered. The methodology and software were applied to a real dataset of burned area in Galicia for the period 1999–2008. The burned area in Galicia increases strongly during summer periods. Forest managers are interested in predicting the burned area to manage resources more efficiently. The two semiparametric models are analysed and compared with a purely parametric model. In terms of error, the most successful results are provided by the first semiparametric time-series model.Ministerio del Medio Ambiente, Rural y Marino; PSE-310000-2009-4Ministerio de Economía y Competitividad; MTM2014-52876-RMinisterio de Economía y Competitividad; MTM2011-22392Ministerio de Economía y Competitividad; MTM2013-41383-PXunta de Galicia; CN2012/130Xunta de Galicia; 07MRU035291PRCOST Action/UE COST-OC-2008-1-2124

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table