EARLINET: towards an advanced sustainable European aerosol lidar network

The European Aerosol Research Lidar Network, EARLINET, was founded in 2000 as a research project for establishing a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and temporal distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive collection of ground-based data for the aerosol vertical distribution over Europe. This paper gives an overview of the network's main developments since 2000 and introduces the dedicated EARLINET special issue, which reports on the present innovative and comprehensive technical solutions and scientific results related to the use of advanced lidar remote sensing techniques for the study of aerosol properties as developed within the network in the last 13 years. Since 2000, EARLINET has developed greatly in terms of number of stations and spatial distribution: from 17 stations in 10 countries in 2000 to 27 stations in 16 countries in 2013. EARLINET has developed greatly also in terms of technological advances with the spread of advanced multiwavelength Raman lidar stations in Europe. The developments for the quality assurance strategy, the optimization of instruments and data processing, and the dissemination of data have contributed to a significant improvement of the network towards a more sustainable observing system, with an increase in the observing capability and a reduction of operational costs. Consequently, EARLINET data have already been extensively used for many climatological studies, long-range transport events, Saharan dust outbreaks, plumes from volcanic eruptions, and for model evaluation and satellite data validation and integration. Future plans are aimed at continuous measurements and near-real-time data delivery in close cooperation with other ground-based networks, such as in the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) www.actris.net, and with the modeling and satellite community, linking the research community with the operational world, with the aim of establishing of the atmospheric part of the European component of the integrated global observing system.Peer ReviewedPostprint (published version

Moving Beyond Static Species Distribution Models in Support of Conservation Biogeography

Aim: To demonstrate that multi-modeling methods have effectively been used to combine static species distribution models (SDM), predicting the geographical pattern of suitable habitat, with dynamics landscape and population models in order to forecast the impacts of environmental change on species, an important goal of conservation biogeography. Methods: Three approaches were considered: a) incorporating models of species migration in order to understand the ability of a species to occupy suitable habitat in new locations; b) linking models of landscape disturbance and succession to models of habitat suitability; and, c) fully linking models of habitat suitability, habitat dynamics and spatially-explicit population dynamics. Results: Linking species-environment relationships, landscape dynamics and population dynamics in a multi-modeling framework allows the combined impacts of climate change (affecting species distribution and vital rates) and land cover dynamics (land use change, altered disturbance regimes) on species be predicted. This approach is only feasible if the life history parameters and habitat requirements of the species are well understood. Main Conclusions: Forecasts of the impacts of global change on species have been improved by considering multiple causes. A range of methods are available to address the interactions of changing habitat suitability, habitat dynamics and population response that vary in their complexity, realism and data requirements.

Short-term fire front spread prediction using inverse modelling and airborne infrared images

A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.Peer ReviewedPostprint (author's final draft

Software fires detection and extinction for forest

This article shows the most usual fire detection and forest extinction application technologies at present. We will see all different methods used by these applications that can be found in the Market and some examples. Also, some basic questions about the most influent parameters when a fire must be extinct are shown. Finally, after having shown all the technologies, we will build a model about an intelligent system which not only detects, but also extinguish wildfire

A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation 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 behvaiour 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 a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Advances in UAS for forest fire fighting

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Project SKY-EYE: applying UAVs to forest fire fighter support and monitoring

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