This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and ...modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.
The flood risk is a function of the flood hazard, the exposed values, and their vulnerability. In addition to extreme hydrological events, different anthropogenic activities such as extensive ...urbanization and land use play an important role in producing catastrophic floods. Considerations of both physical and social dimensions are therefore equally important in flood risk assessment. However, very often the risk assessment studies focus either on physical or social dimensions. In addition, the available studies often focus on economic valuation of only direct tangible costs. In this study, we provide an integrated flood risk assessment approach that goes beyond the valuation of direct tangible costs, through incorporating physical dimensions in hazard and exposure and social dimensions in vulnerability. The method has been implemented in the Dhaka City, Bangladesh, an area internationally recognized as hot spot for flood risk. In this study, flood hazards for different return periods are calculated in spatial environment using a hydrologic model, HEC-RAS. Vulnerability is assessed through aggregation of various social dimensions, i.e., coping and adaptive capacities, and susceptibility. We assess vulnerability for both baseline and improved scenarios. In the baseline scenario, current early warning for study area is considered. In the alternative scenario, the warning system is expected to improve. Aggregating hazard, exposure and vulnerability, risk maps (in terms of both tangible and intangible costs) of several return period floods are produced for both baseline and improved scenarios. Compared to traditional assessments, the integrated assessment approach used in this study generates more information about the flood risk. Consequently, the results are useful in evaluating policy alternatives and minimizing property loss in the study area.
Enhancing the governance of social-ecological systems for more equitable and sustainable development is hindered by inadequate knowledge about how different social groups and communities rely on ...natural resources. We used openly accessible national survey data to develop a metric of overall dependence on natural resources. These data contain information about households' sources of water, energy, building materials and food. We used these data in combination with Bayesian learning to model observed patterns of dependence using demographic variables that included: gender of household head, household size, income, house ownership, formality status of settlement, population density, and in-migration rate to the area. We show that a small number of factors-in particular population density and informality of settlements-can explain a significant amount of the observed variation with regards to the use of natural resources. Subsequently, we test the validity of these predictions using alternative, open access data in the eThekwini and Cape Town metropolitan areas of South Africa. We discuss the advantages of using a selection of predictors which could be supplied through remotely sensed and open access data, in terms of opportunities and challenges to produce meaningful results in data-poor areas. With data availability being a common limiting factor in modelling and monitoring exercises, access to inexpensive, up-to-date and free to use data can significantly improve how we monitor progress towards sustainability targets. A small selection of openly accessible demographic variables can predict household's dependence on local natural resources.
The Sustainable Development Goals aim at ending food insecurity by 2030. Therefore, civil society needs to understand the inherent complexities of both socio-economic and ecological dynamics and ...their interdependencies. In particular, the behavioural dynamics that underpin human agents are crucial in driving the final outcomes in terms of community food security and require further attention. Using household behaviour within a rural village of Southern Malawi as an example, we describe a game theory model representing cropping strategies: (1) cooperation, as driven by other-regarding preferences, and (2) conformation, the tendency to converge to similar crop planting choices as opposed to differentiation (and thus crop diversity). We find that the latter plays a crucial role in driving the system towards successful strategies: how individuals relate to social norms has greater effect. Cooperation is only necessary for community success when the community converges on crop planting choices. On the contrary, differentiation, the affirmation of the individual unique identity, can succeed with or without cooperation. We further elaborate on the idea that community level sustainability can be reached through different pathways, which might require food exchange mechanisms within and beyond the system boundaries.
Urbanization and agricultural intensification are the main drivers of biodiversity losses through multiple stressors, especially habitat fragmentation, isolation and loss. Designing Blue and Green ...Infrastructure Networks (BGIN) has been recommended as a potential tool for land-use planning to increase ecosystem services while preserving biodiversity. All municipalities in France are required to perform BGIN planning. This article focuses on the Couesnon watershed (Brittany, France) and the participatory process used to define and analyze five possible pathways of future land-use and land-cover changes that included implementation of BGINs. Impacts on biodiversity were estimated by quantifying the change in landscape connectivity of woodlands, grasslands and wetlands. The effectiveness of BGIN policies was assessed by comparing current landscape connectivity (2018) to those in possible futures. Landscape connectivity referred to functional connectivity for three indicator species (Abax parallelepipedus, Maniola jurtina and Arvicola sapidus) across three landscape features: woodlands, grasslands and wetlands, respectively. Results allowed impacts of urban and agricultural land-use changes to be identified in terms of extent and quality. If BGIN policies were applied effectively to control the expansion of gray infrastructure, they would help increase the area and the quality of grassland and woodland connectivity by no more than 2%. Agricultural land-use and land-cover changes could have more impact on the extent of grassland (−82% to +38%) and wetland (−49% to +47%) connectivity. Current and future trends for hedgerows implied a decrease in woodland connectivity of 9.8–33.8%. Impacts on the quality of landscape connectivity is not proportional with the extent, as a decrease of the latter can have relatively more negative impacts on the former, and inversely. The study highlights that the BGIN strategy can preserve landscape connectivity effectively in urban ecosystems, where human density is higher, but can be threatened by agricultural intensification.
•EU Blue and Green Infrastructure strategy aims at limiting gray infrastructure.•LUCC scenarios are developed to assess the BGIN efficiency over the long term.•LUCC simulations are used to evaluate the impact on landscape connectivity.•BGIN strategy is efficient to limit land artificialization.•Future agricultural land uses can counteract BGIN effectiveness.
Los avances tecnológicos y metodológicos de las últimas décadas (e.g., información satelital, potencia de los ordenadores, análisis geoespacial, desarrollo de algoritmos) facilitaron buscar ...soluciones a problemas complejos como el cambio global. Estos avances permitieron que surjan plataformas informáticas para modelar servicios ecosistémicos, que cuantifican los beneficios de la naturaleza y evalúan cómo son o serán afectados por acciones humanas. Actualmente, existen variadas plataformas con diferentes grados de aptitud según el contexto, destacándose k.LAB por ser gratuita, de código abierto y presentar un enfoque de ciencia colaborativa, además de integrar diferentes técnicas de modelado con inteligencia artificial. k.LAB es muy versátil para responder a las demandas de diferentes usos, desde programar y modelar SE hasta tomar decisiones. Sin embargo, quienes cuantifican y mapean SE, especialmente en Latinoamérica, tienen escaso conocimiento de k.LAB; esto dificulta aprovechar su potencial, tal como sucedió con herramientas de acceso libre y código abierto (e.g., la adopción de R requirió tiempo, revisiones, discusiones y materiales didácticos en revistas especializadas). Este trabajo presenta las capacidades de k.LAB en el contexto de las plataformas de modelado de SE. Primero, introducimos estas plataformas en términos generales, con énfasis en las más usadas. Luego, caracterizamos k.LAB técnica y filosóficamente. Después, presentamos un caso de estudio en el norte de la Patagonia argentina, ilustrando la obtención de mapas de tres SE (captura de carbono, polinización y recreación al aire libre) utilizando aplicaciones de modelado dirigidas a personas sin experiencia en programación. Finalmente, establecemos características deseables en las plataformas de modelado de SE para discutir ventajas y limitaciones de k.LAB en relación con otras alternativas. Esperamos brindar un marco general útil para el modelado de SE y ampliar el conjunto de herramientas para abordar problemáticas vinculadas al cambio global en la Argentina y otros países de la región.
Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration ...and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure--i.e., public data and model repositories--is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making. Keywords: Global challenges, Sustainability, Artificial intelligence, Semantics, Knowledge integration and synthesis
In this experimental study, different components are computed for three different ecosystem services (ES). Specifically, supply, demand and use are estimated for pollination service, flood risk ...regulation service and nature-based tourism. These are analysed and assessed in 2012 and 2018 for the Italian context, in order to estimate the evolution over this period and to allow a significant comparison of results. The same methodology and models are applied for the selected accounting years and accounting tables and tend to reflect as closely as possible the System of Environmental-Economic Accounting-Ecosystem Accounting (SEEA EA), which is the international standard endorsed by the United Nations to compile Natural Capital Accounting in 2021. Both biophysical and monetary assessments are performed using the ARIES technology, an integrated modelling platform providing automatic and flexible integration of data and models, via its semantic modelling nature. Models have been run adjusting the components of the global modelling approach to the Italian context and, whenever available, prioritising the use of local data to carry out the study. This approach is particularly useful to analyse trends over time, as potentially biased components of models and data are substantially mitigated when the same biases is constant over time. This study finds an increase in benefits over the period analysed for the ES examined. The main contribution of this pioneering work is to support the idea that ES accounting or Natural Capital Accounting can provide a very useful tool to improve economic and environmental information at national and regional level. This can support processes to provide the necessary incentives to steer policy-making towards preventative rather than corrective actions, which are usually much less effective and more costly, both at environmental and economic levels. Nevertheless, particular attention must be paid to the meaning of the estimates and the drivers of these values to derive a direct or indirect relationship between the benefits observable and the actual Italian ecosystems condition.
A vast body of literature suggests that the European Alpine Region is extremely sensitive to climate change. Winter tourism is closely related to climate variations, especially in mountain regions ...where resorts are heavily dependent on snow. This paper explores how to effectively integrate a climate change adaptation perspective with local discourses about sustainability and tourism, an increasing priority for policy-makers in the region and elsewhere. It reports on the development and application of a participatory decision support process for the analysis of adaptation strategies for local development of an Alpine tourism destination, Auronzo di Cadore (Dolomites, Italy). This experience significantly contributed to the idea that an efficient combination of modelling capabilities, decision support tools, and participatory processes can substantially improve decision-making for sustainability. The authors show that, in this case study, such a combination of methods and tools allowed for managing the involvement of local actors, stimulating local debates on climate change adaptation and possible consequences on winter tourism, encouraging creativity and smoothing potential conflicts, and easing the integration of the qualitative knowledge and the preferences of the involved actors with quantitative information. This contributed to an integrated sustainability assessment of alternative strategies for sustainable tourism planning.
Farmers’ irrigation practices play a crucial role in the sustainability of crop production and water consumption, and in the way they deal with the current and future effects of climate change. In ...this study, a system dynamic multi-agent model adopting the soil water balance provided by the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 was developed to explore how farmers’ decision making may affect future water needs and use with a focus on the role of climate services, i.e. forecasts and insurance. A climatic projection record representing the down-scaled A1B market scenario (a balance across all sources) of the assessment report of the Intergovernmental Panel on Climate Change (IPCC) is used to produce future daily data about relative humidity, precipitation, temperature and wind speed. Two types of meteorological services are made available: i) a bi-weekly bulletin; and ii) seasonal forecasts. The precision of these services was altered to represent different conditions, from perfect knowledge to poor forecasts. Using the available forecasts, farming agents take adaptation decisions concerning crop allocation and irrigation management on the basis of their own risk attitudes. Farmers’ attitudes are characterized by fuzzy classifications depending on age, relative income and crop profitability. Farming agents’ adaptation decisions directly affect the crop and irrigation parameters, which in turn affect future water needs on a territorial level. By incorporating available and future meteorological services, the model allows the farmer’s decision making-process to be explored together with the consequent future irrigation water demand for the period 2015 to 2030. The model prototype is applied to a data set of the Venice Lagoon Watershed, an area of 2038 km2 in north-east Italy, for a preliminary test of its performance and to design future development objectives.
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