A new wildland fire danger index for a Mediterranean region and some validation aspects

Wildland fires are the main cause of tree mortality in Mediterranean Europe and a major threat to Spanish forests. This paper focuses on the design and validation of a new wildland fire index especially adapted to a Mediterranean Spanish region. The index considers ignition and spread danger components. Indicators of natural and human ignition agents, historical occurrence, fuel conditions and fire spread make up the hierarchical structure of the index. Multi-criteria methods were used to incorporate experts¿ opinion in the process of weighting the indicators and to carry out the aggregation of components into the final index, which is used to map the probability of daily fire occurrence on a 0.5-km grid. Generalised estimating equation models, which account for possible correlated responses, were used to validate the index, accommodating its values onto a larger scale because historical records of daily fire occurrence, which constitute the dependent variable, are referred to cells on a 10-km grid. Validation results showed good index performance, good fit of the logistic model and acceptable discrimination power. Therefore, the index will improve the ability of fire prevention services in daily allocation of resources.The authors acknowledge the support received from the Ministry of Science and Innovation through the research project Modelling and Optimisation Techniques for a Sustainable Development, Ref. EC02008-05895-C02-01/ECON.Vicente López, FJD.; Crespo Abril, F. (2012). A new wildland fire danger index for a Mediterranean region and some validation aspects. International Journal of Wildland Fire. 21(8):1030-1041. https://doi.org/10.1071/WF11046S10301041218Aguado, I., Chuvieco, E., Borén, R., & Nieto, H. (2007). Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. International Journal of Wildland Fire, 16(4), 390. doi:10.1071/wf06136Andrews, P. L., Loftsgaarden, D. O., & Bradshaw, L. S. (2003). Evaluation of fire danger rating indexes using logistic regression and percentile analysis. International Journal of Wildland Fire, 12(2), 213. doi:10.1071/wf02059Bradstock, R. A., Cohn, J. S., Gill, A. M., Bedward, M., & Lucas, C. (2009). Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather. International Journal of Wildland Fire, 18(8), 932. doi:10.1071/wf08133Buizza, R., & Hollingsworth, A. (2002). Storm prediction over Europe using the ECMWF Ensemble Prediction System. Meteorological Applications, 9(3), 289-305. doi:10.1017/s1350482702003031Carmel, Y., Paz, S., Jahashan, F., & Shoshany, M. (2009). Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management, 257(1), 370-377. doi:10.1016/j.foreco.2008.09.039Castedo-Dorado, F., Rodríguez-Pérez, J. R., Marcos-Menéndez, J. L., & Álvarez-Taboada, M. F. (2011). Modelling the probability of lightning-induced forest fire occurrence in the province of León (NW Spain). Forest Systems, 20(1), 95. doi:10.5424/fs/2011201-9409Catry, F. X., Rego, F. C., Bação, F. L., & Moreira, F. (2009). Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8), 921. doi:10.1071/wf07123CHUVIECO, E., & SALAS, J. (1996). Mapping the spatial distribution of forest fire danger using GIS. International journal of geographical information systems, 10(3), 333-345. doi:10.1080/02693799608902082Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martı́nez-Vega, J., de la Riva, J., & Pérez, F. (2004). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92(3), 322-331. doi:10.1016/j.rse.2004.01.019Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martín, M. P., … Zamora, R. (2010). Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling, 221(1), 46-58. doi:10.1016/j.ecolmodel.2008.11.017Danson, F. M., & Bowyer, P. (2004). Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment, 92(3), 309-321. doi:10.1016/j.rse.2004.03.017Dasgupta, S., Qu, J. J., & Hao, X. (2006). Design of a Susceptibility Index for Fire Risk Monitoring. IEEE Geoscience and Remote Sensing Letters, 3(1), 140-144. doi:10.1109/lgrs.2005.858484Fairbrother, A., & Turnley, J. G. (2005). Predicting risks of uncharacteristic wildfires: Application of the risk assessment process. Forest Ecology and Management, 211(1-2), 28-35. doi:10.1016/j.foreco.2005.01.026Finney, M. A. (2005). The challenge of quantitative risk analysis for wildland fire. Forest Ecology and Management, 211(1-2), 97-108. doi:10.1016/j.foreco.2005.02.010Gouma, V., & Chronopoulou-Sereli, A. (1998). Wildland Fire Danger Zoning - a Methodology. International Journal of Wildland Fire, 8(1), 37. doi:10.1071/wf9980037Hernandez-Leal, P. A., Arbelo, M., & Gonzalez-Calvo, A. (2006). Fire risk assessment using satellite data. Advances in Space Research, 37(4), 741-746. doi:10.1016/j.asr.2004.12.053Li, L.-M., Song, W.-G., Ma, J., & Satoh, K. (2009). Artificial neural network approach for modeling the impact of population density and weather parameters on forest fire risk. International Journal of Wildland Fire, 18(6), 640. doi:10.1071/wf07136Maingi, J. K., & Henry, M. C. (2007). Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. International Journal of Wildland Fire, 16(1), 23. doi:10.1071/wf06007Martell, D. L., Otukol, S., & Stocks, B. J. (1987). A logistic model for predicting daily people-caused forest fire occurrence in Ontario. Canadian Journal of Forest Research, 17(5), 394-401. doi:10.1139/x87-068Martínez, J., Vega-Garcia, C., & Chuvieco, E. (2009). Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management, 90(2), 1241-1252. doi:10.1016/j.jenvman.2008.07.005Moffett, A., Garson, J., & Sarkar, S. (2005). MultCSync: a software package for incorporating multiple criteria in conservation planning. Environmental Modelling & Software, 20(10), 1315-1322. doi:10.1016/j.envsoft.2004.10.001Nieto, H., Aguado, I., Chuvieco, E., & Sandholt, I. (2010). Dead fuel moisture estimation with MSG–SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content. Agricultural and Forest Meteorology, 150(7-8), 861-870. doi:10.1016/j.agrformet.2010.02.007Noble, B. F., & Christmas, L. M. (2007). Strategic Environmental Assessment of Greenhouse Gas Mitigation Options in the Canadian Agricultural Sector. Environmental Management, 41(1), 64-78. doi:10.1007/s00267-007-9017-yNúñez-Regueira, L. (1997). Calorific values and flammability of forest species in galicia. Continental high mountainous and humid Atlantic zones. Bioresource Technology, 61(2), 111-119. doi:10.1016/s0960-8524(97)00053-9Padilla, M., & Vega-García, C. (2011). On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain. International Journal of Wildland Fire, 20(1), 46. doi:10.1071/wf09139Pendergast, J. F., Gange, S. J., Newton, M. A., Lindstrom, M. J., Palta, M., & Fisher, M. R. (1996). A Survey of Methods for Analyzing Clustered Binary Response Data. International Statistical Review / Revue Internationale de Statistique, 64(1), 89. doi:10.2307/1403425Pew, K. ., & Larsen, C. P. . (2001). GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rain forest of Vancouver Island, Canada. Forest Ecology and Management, 140(1), 1-18. doi:10.1016/s0378-1127(00)00271-1Podur, J., Martell, D. L., & Csillag, F. (2003). Spatial patterns of lightning-caused forest fires in Ontario, 1976–1998. Ecological Modelling, 164(1), 1-20. doi:10.1016/s0304-3800(02)00386-1Preisler, H. K., Brillinger, D. R., Burgan, R. E., & Benoit, J. W. (2004). Probability based models for estimation of wildfire risk. International Journal of Wildland Fire, 13(2), 133. doi:10.1071/wf02061Preisler, H. K., Chen, S.-C., Fujioka, F., Benoit, J. W., & Westerling, A. L. (2008). Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices. International Journal of Wildland Fire, 17(3), 305. doi:10.1071/wf06162Romero-Calcerrada, R., Novillo, C. J., Millington, J. D. A., & Gomez-Jimenez, I. (2008). GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain). Landscape Ecology, 23(3), 341-354. doi:10.1007/s10980-008-9190-2Saaty, T. L. (1987). RANK GENERATION, PRESERVATION, AND REVERSAL IN THE ANALYTIC HIERARCHY DECISION PROCESS. Decision Sciences, 18(2), 157-177. doi:10.1111/j.1540-5915.1987.tb01514.xSahin, Y. G., & Ince, T. (2009). Early Forest Fire Detection Using Radio-Acoustic Sounding System. Sensors, 9(3), 1485-1498. doi:10.3390/s90301485López, A. S., San-Miguel-Ayanz, J., & Burgan, R. E. (2002). Integration of satellite sensor data, fuel type maps and meteorological observations for evaluation of forest fire risk at the pan-European scale. International Journal of Remote Sensing, 23(13), 2713-2719. doi:10.1080/01431160110107761Sharples, J. J., McRae, R. H. D., Weber, R. O., & Gill, A. M. (2009). A simple index for assessing fire danger rating. Environmental Modelling & Software, 24(6), 764-774. doi:10.1016/j.envsoft.2008.11.004Stocks, B. J., Lynham, T. J., Lawson, B. D., Alexander, M. E., Wagner, C. E. V., McAlpine, R. S., & Dubé, D. E. (1989). The Canadian Forest Fire Danger Rating System: An Overview. The Forestry Chronicle, 65(6), 450-457. doi:10.5558/tfc65450-6Sturtevant, B. R., & Cleland, D. T. (2007). Human and biophysical factors influencing modern fire disturbance in northern Wisconsin. International Journal of Wildland Fire, 16(4), 398. doi:10.1071/wf06023Swets, J. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285-1293. doi:10.1126/science.3287615Vadrevu, K. P., Eaturu, A., & Badarinath, K. V. S. (2009). Fire risk evaluation using multicriteria analysis—a case study. Environmental Monitoring and Assessment, 166(1-4), 223-239. doi:10.1007/s10661-009-0997-3Vasilakos, C., Kalabokidis, K., Hatzopoulos, J., Kallos, G., & Matsinos, Y. (2007). Integrating new methods and tools in fire danger rating. International Journal of Wildland Fire, 16(3), 306. doi:10.1071/wf05091Verde, J. C., & Zêzere, J. L. (2010). Assessment and validation of wildfire susceptibility and hazard in Portugal. Natural Hazards and Earth System Science, 10(3), 485-497. doi:10.5194/nhess-10-485-2010Wotton, B. M., & Martell, D. L. (2005). A lightning fire occurrence model for Ontario. Canadian Journal of Forest Research, 35(6), 1389-1401. doi:10.1139/x05-071Yebra, M., Chuvieco, E., & Riaño, D. (2008). Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology, 148(4), 523-536. doi:10.1016/j.agrformet.2007.12.00

Anti-tumour necrosis factor discontinuation in inflammatory bowel disease patients in remission: study protocol of a prospective, multicentre, randomized clinical trial

Background: Patients with inflammatory bowel disease who achieve remission with anti-tumour necrosis factor (anti-TNF) drugs may have treatment withdrawn due to safety concerns and cost considerations, but there is a lack of prospective, controlled data investigating this strategy. The primary study aim is to compare the rates of clinical remission at 1?year in patients who discontinue anti-TNF treatment versus those who continue treatment. Methods: This is an ongoing, prospective, double-blind, multicentre, randomized, placebo-controlled study in patients with Crohn?s disease or ulcerative colitis who have achieved clinical remission for ?6?months with an anti-TNF treatment and an immunosuppressant. Patients are being randomized 1:1 to discontinue anti-TNF therapy or continue therapy. Randomization stratifies patients by the type of inflammatory bowel disease and drug (infliximab versus adalimumab) at study inclusion. The primary endpoint of the study is sustained clinical remission at 1?year. Other endpoints include endoscopic and radiological activity, patient-reported outcomes (quality of life, work productivity), safety and predictive factors for relapse. The required sample size is 194 patients. In addition to the main analysis (discontinuation versus continuation), subanalyses will include stratification by type of inflammatory bowel disease, phenotype and previous treatment. Biological samples will be obtained to identify factors predictive of relapse after treatment withdrawal. Results: Enrolment began in 2016, and the study is expected to end in 2020. Conclusions: This study will contribute prospective, controlled data on outcomes and predictors of relapse in patients with inflammatory bowel disease after withdrawal of anti-TNF agents following achievement of clinical remission. Clinical trial reference number: EudraCT 2015-001410-1

A search for an unexpected asymmetry in the production of e+μ− and e−μ+ pairs in proton-proton collisions recorded by the ATLAS detector at root s = 13 TeV

This search, a type not previously performed at ATLAS, uses a comparison of the production cross sections for e(+)mu(-) and e(-)mu(+) pairs to constrain physics processes beyond the Standard Model. It uses 139 fb(-1) of proton-proton collision data recorded at root s = 13 TeV at the LHC. Targeting sources of new physics which prefer final states containing e(+)mu(-) and e(-)mu(+), the search contains two broad signal regions which are used to provide model-independent constraints on the ratio of cross sections at the 2% level. The search also has two special selections targeting supersymmetric models and leptoquark signatures. Observations using one of these selections are able to exclude, at 95% confidence level, singly produced smuons with masses up to 640 GeV in a model in which the only other light sparticle is a neutralino when the R-parity-violating coupling lambda(23)(1)' is close to unity. Observations using the other selection exclude scalar leptoquarks with masses below 1880 GeV when g(1R)(eu) = g(1R)(mu c) = 1, at 95% confidence level. The limit on the coupling reduces to g(1R)(eu) = g(1R)(mu c) = 0.46 for a mass of 1420 GeV

Measurement of the nuclear modification factor for muons from charm and bottom hadrons in Pb+Pb collisions at 5.02 TeV with the ATLAS detector

Heavy-flavour hadron production provides information about the transport properties and microscopic structure of the quark-gluon plasma created in ultra-relativistic heavy-ion collisions. A measurement of the muons from semileptonic decays of charm and bottom hadrons produced in Pb+Pb and pp collisions at a nucleon-nucleon centre-of-mass energy of 5.02 TeV with the ATLAS detector at the Large Hadron Collider is presented. The Pb+Pb data were collected in 2015 and 2018 with sampled integrated luminosities of 208 mu b(-1) and 38 mu b(-1), respectively, and pp data with a sampled integrated luminosity of 1.17 pb(-1) were collected in 2017. Muons from heavy-flavour semileptonic decays are separated from the light-flavour hadronic background using the momentum imbalance between the inner detector and muon spectrometer measurements, and muons originating from charm and bottom decays are further separated via the muon track's transverse impact parameter. Differential yields in Pb+Pb collisions and differential cross sections in pp collisions for such muons are measured as a function of muon transverse momentum from 4 GeV to 30 GeV in the absolute pseudorapidity interval vertical bar eta vertical bar < 2. Nuclear modification factors for charm and bottom muons are presented as a function of muon transverse momentum in intervals of Pb+Pb collision centrality. The bottom muon results are the most precise measurement of b quark nuclear modification at low transverse momentum where reconstruction of B hadrons is challenging. The measured nuclear modification factors quantify a significant suppression of the yields of muons from decays of charm and bottom hadrons, with stronger effects for muons from charm hadron decays