Multi-scale Robust Modelling of Landslide Susceptibility: Regional Rapid Assessment and Catchment Robust Fuzzy Ensemble

IFIP Advances in Information and Communication Technology

Barredo

Busetto

et al. 2013

Self Cite

105

Abstract. Forest ecosystems play a key role in the global carbon cycle. Spatially explicit data and assessments of forest biomass and carbon are therefore crucial for designing and implementing effective sustainable forest management options and forest related policies. In this contribution, we present European-wide maps of forest biomass and carbon stock spatially disaggregated at 1km x 1km. The maps originated from a spatialisation improvement of the IPCC methodology for estimating the forest biomass at IPCC Tier 1 level (IPCC-T1). Using a categorical map of ecological zones within the mapping technique may originate boundary effects between the ecological zones. This may induce undue artifacts in the outcomes, as evident in previously published maps generated with the IPCC-T1 methodology. Here we present a novel method for IPCC-T1 biomass mapping which mitigates these artifacts. We propose the use of a fuzzy similarity map of the FAO ecological zones computed by estimating the relative distance similarity (RDS) of each grid-cells climate and geography with respect to the FAO ecological zones. A robust ensemble approach was used to merge an array of simple models with spatially distributed fuzzy set-membership. This allowed the boundary artifacts to be reduced, while mitigating the impact of model semantic extrapolation. The chain of semantically enhanced data-transformations is described following the semantic array programming paradigm. Preliminary results obtained from the application of this novel approach are presented along with a discussion of its impact on the derived maps.

Section: Resultsmentioning

confidence: 99%

Continental-Scale Living Forest Biomass and Carbon Stock: A Robust Fuzzy Ensemble of IPCC Tier 1 Maps for Europe

IFIP Advances in Information and Communication Technology

Barredo

Busetto

et al. 2013

Self Cite

105

“…The induced land cover degradation [34] has also a potential role in exacerbating soil erosion [18,19] and shallow landslides [17] while altering the precipitation-runoff relationship [35] along with flood regimes [36,37]. Off-site effects [38,39] such as fire-induced sediment yield in downstream rivers may cause losses in water storage and affect water quality [40,41] and long-range transport of aerosol may induce transboundary effects [10,11].…”

Section: Transdisciplinary Wildfire Behaviour: Continental Scale Inrmmmentioning

confidence: 99%

“…Further studies are still ongoing for overcoming the limits of the SINMAP approach. A more accurate aposteriori analysis is proposed in [17] where a new fuzzy ensemble is applied for aggregating an array of different landslide susceptibility models. Soil Erosion.…”

Section: Assessing Wildfire Impactsmentioning

confidence: 99%

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An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

IFIP Advances in Information and Communication Technology

Rodriguez-Aseretto

Bosco

et al. 2013

Self Cite

Abstract. Wildfires in Europe -especially in the Mediterranean region -are one of the major treats at landscape scale. While their immediate impact ranges from endangering human life to the destruction of economic assets, other damages exceed the spatio-temporal scale of a fire event. Wildfires involving forest resources are associated with intense carbon emissions and alteration of surrounding ecosystems. The induced land cover degradation has also a potential role in exacerbating soil erosion and shallow landslides. A component of the complexity in assessing fire impacts resides in the difference between uncontrolled wildfires and those for which a control strategy is applied. Robust modelling of wildfire behaviour requires dynamic simulations under an array of multiple fuel models, meteorological disturbances and control strategies for mitigating fire damages. Uncertainty is associated to meteorological forecast and fuel model estimation. Software uncertainty also derives from the datatransformation models needed for predicting the wildfire behaviour and its consequences. The complex and dynamic interactions of these factors define a context of deep uncertainty. Here an architecture for adaptive and robust modelling of wildfire behaviour is proposed, following the semantic array programming paradigm. The mathematical conceptualisation focuses on the dynamic exploitation of updated meteorological information and the design flexibility in adapting to the heterogeneous European conditions. Also, the modelling architecture proposes a multicriteria approach for assessing the potential impact with qualitative rapid assessment methods and more accurate a-posteriori assessment.

“…Fire-induced geomorphic and ecological transformations [6][7][8] may cause severe carbon emissions and alter biodiversity [9,10] even due to habitat fragmentation [11][12][13]. They may also affect landslide susceptibility [14] and soil erosion [15,16] with potential off-site impacts [17,18] on water resources [19] (e.g. on floods [20,21] and water quality [22]).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data Driven Ensemble for Wildfire Behaviour Assessment: A Case Study

Leo

IFIP Advances in Information and Communication Technology

Rodriguez-Aseretto

et al. 2013

Self Cite

Abstract. Wildfire information has long been collected in Europe, with particular focus on forest fires. The European Forest Fire Information System (EFFIS) of the European Commission complements and harmonises the information collected by member countries and covers the forest fire management cycle. This latter ranges from forest fire preparedness to post-fire impact analysis. However, predicting and simulating fire event dynamics requires the integrated modelling of several sources of uncertainty. Here we present a case study of a novel conceptualization based on a Semantic Array Programming (SemAP) application of the Dynamic Data Driven Application Systems (DDDAS) concept. The case study is based on a new architecture for adaptive and robust modelling of wildfire behaviour. It focuses on the module for simulating wildfire dynamics under fire control scenarios. Rapid assessment of the involved impact due to carbon emission and potential soil erosion is also shown. Uncertainty is assessed by ensembling an array of simulations which consider the uncertainty in meteorology, fuel, software modules. The event under investigation is a major wildfire occurred in 2012, widely reported as one of the worst in the Valencia region, Spain. The inherent data, modelling and software uncertainty are discussed and preliminary results of the robust data-driven ensemble application are presented. The case study suitably illustrates a typical modelling context in many European areas -for which timely collecting accurate local information on vegetation, fuel, humidity, wind fields is not feasible -where robust and flexible approaches may prove as a viable modelling strategy.