Claudio Bosco

European digital archive on soil maps (EuDASM): preserving important soil data for public free access

Abstract Historical soil survey paper maps are valuable resources that underpin strategies to support soil protection and promote sustainable land use practices, especially in developing countries where digital soil information is often missing. However, many of the soil maps, in particular those for developing countries, are held in traditional archives that are not easily accessible to potential users. Additionally, many of these documents are over 50 years old and are beginning to deteriorate. Realising the need to conserve this information, the Joint Research Centre (JRC) and the ISRIC-World Soil Information foundation have created the European Digital Archive of Soil Maps (EuDASM), through which all archived paper maps of ISRIC has been made accessible to the public through the Internet. The immediate objective is to transfer paper-based soil maps into a digital format with the maximum possible resolution and to ensure their preservation and easy disclosure. More than 6,000 maps from 135 countries have been captured and are freely available to users through a user-friendly web-based interface. Initial feedback has been very positive, especially from users in Africa, South America and Asia to whom archived soil maps were made available to local users, often for the first time. Link: http://eusoils.jrc.ec.europa.eu/library/maps/country_maps/list_countries.cfm

Land take and food security: assessment of land take on the agricultural production in Europe

Soil is a multifunctional, non-renewable natural resource for Europe as clearly expressed in the European Union (EU) Thematic Strategy for Soil Protection (COM (2006)231). Soil carries out multiple functions, including the support of food production. Urban development and its associated land take poses a major threat to soil and could have significant effects on agricultural production. This paper aims to evaluate the potential productivity losses in European agriculture due to land-take processes between 1990 and 2006. Agricultural land take was calculated using CORINE Land Cover maps of 1990, 2000 and 2006. For 21 of the 27 EU member states, agricultural land take was computed to be 752,973 ha for 1990–2000 and 436,095 ha for 2000–2006, representing 70.8% and 53.5%, respectively, of the total EU land take for these periods. The impact of this land take on the production capabilities of the agricultural sector for the period 1990–2006 for 19 of the 21 states was estimated to be equivalent to a loss of more than six million tonnes of wheat. The paper demonstrates that Europes intense urbanisation has a direct impact on its capability to produce food.

Architecture of a Pan-European Framework for Integrated Soil Water Erosion Assessment

Soil erosion implications on future food security are gaining global attention because in many areas worldwide there is an imbalance between soil loss and its subsequent deposition. Soil erosion is a complex phenomenon af fected by many factors such as climate, topography and land cover (in particular forest resources, natural vegetation and agriculture) while directly influencing water sediment transport, the quality of water resources and water storage loss. A modeling architecture, based on the Revised Universal Soil Loss Equation, is proposed and applied to evaluate and validate at regional scale potential and actual soil water erosion, enabling it to be linked to other involved natural resources. The methodology benefits from the array programming paradigm with semantic constraints (lightweight array behavioural contracts provided by the Mastrave library) to concisely implement models as composition of interoperable modules and to process heterogeneous data.

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

Landslide susceptibility assessment is a fundamental component of effective landslide prevention. One of the main challenges in landslides forecasting is the assessment of spatial distribution of landslide susceptibility. Despite the many different approaches, landslide susceptibility assessment still remains a challenge. A semi-quantitative method is proposed combining heuristic, deterministic and probabilistic approaches for a robust catchment scale assessment. A fuzzy ensemble model has been exploited for aggregating an array of different susceptibility zonation maps. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN) and two different landslide susceptibility techniques based on the infinite slope stability model. The sequence of data-transformation models has been enhanced following the semantic array programming paradigm. The ensemble has been applied to a study area in Italy. This catchment scale methodology may be exploited for analysing the potential impact of landscape disturbances. At regional scale, a qualitative approach is also proposed as a rapid assessment technique – suitable for application in real-time operations such as wildfire emergency management.

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

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.

Exploring the high-resolution mapping of gender-disaggregated development indicators

Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.

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

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 data-transformation 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 multi-criteria approach for assessing the potential impact with qualitative rapid assessment methods and more accurate a-posteriori assessment.

Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence

The long-term goal of the global effort to tackle malaria is national and regional elimination and eventually eradication. Fine scale multi-temporal mapping in low malaria transmission settings remains a challenge and the World Health Organisation propose use of surveillance in elimination settings. Here, we show how malaria incidence can be modelled at a fine spatial and temporal resolution from health facility data to help focus surveillance and control to population not attending health facilities. Using Namibia as a case study, we predicted the incidence of malaria, via a Bayesian spatio-temporal model, at a fine spatial resolution from parasitologically confirmed malaria cases and incorporated metrics on healthcare use as well as measures of uncertainty associated with incidence predictions. We then combined the incidence estimates with population maps to estimate clinical burdens and show the benefits of such mapping to identifying areas and seasons that can be targeted for improved surveillance and interventions. Fine spatial resolution maps produced using this approach were then used to target resources to specific local populations, and to specific months of the season. This remote targeting can be especially effective where the population distribution is sparse and further surveillance can be limited to specific local areas.

An analysis of the Land Use Sustainability Index (LUSI) at territorial scale based on Corine Land Cover

Purpose – The aim of this paper is to propose a methodology based on the use of a simple and accessible database, such as Corine Land Cover (CLC), for providing an in depth evaluation of environmental sustainability. This evaluation has been carried out through the analysis of factors such as landscape and habitat composition, the level of biodiversity, the degree of anthropisation and soil sealing and the arable land availability.Design/methodology/approach – Starting from the analysis of some of the existing approaches for the evaluation of environmental sustainability, this paper presents a GIS approach, based on the use of the Corine Land Cover (CLC), and other sources of geographical data, aimed at producing several thematic Environmental Sustainability Indicators, and one synthetic index.Findings – The proposed methodology was found to enable a satisfactory assessment of the environmental state, at territorial scale, starting from an easy accessible land use data set. The adopted approach is tailore...

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 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.