Suchergebnisse

Abstract. Avalanche disasters are associated with significant monetary losses. It is thus crucial that avalanche risk assessments are based on a consistent and proper assessment of the uncertainties involved in the modelling of the avalanche run-out zones and the estimations of the damage potential. We link a Bayesian network (BN) to a Geographic Information System (GIS) for avalanche risk assessment in order to facilitate the explicit modelling of all relevant parameters, their causal relations and the involved uncertainties in a spatially explicit manner. The suggested procedure is illustrated for a case study area (Davos, Switzerland) located in the Swiss Alps. We discuss the potential of such a model by comparing the risks estimated using the probabilistic framework to those obtained by a traditional risk assessment procedure. The presented model may serve as a basis for developing a consistent and unified risk assessment approach.

Ecosystem services assessments have the potential to support negotiating the complex trade-offs between conservation goals and other economic, political and social agendas across administrative borders, spatial and temporal scales. While earlier studies showed the global importance of tropical areas in supplying ecosystem services, the specific contribution of mountain areas has not been investigated in details. The degradation of mountain ecosystems driven by climate, demographic and economic changes is however increasingly threatening essential ecosystem services supply to people living in- and outside mountains. In this study, we present an assessment of eight ecosystem services in mountains across the world using high resolution earth observation datasets for 2000 and 2010. We link the ecosystem services supply data with an expert survey dataset to assess ecosystem services demand. We show that most mountain ranges show large patches of decreasing ecosystem services in areas characterized by high population pressure. By comparing ecosystem services supply of and demand for ecosystem services, we highlight the growing scarcity of highly demanded ecosystem services, in particular water, food and forage in mountain areas of Global South. Population growth in mountain regions and surrounding lowlands accentuate this trend and call for urgent solutions to sustainably manage ecosystems in mountain areas. ; ISSN:2212-0416

ABSTRACT. Agricultural transitions from subsistence to export-oriented production make households more reliant on volatile agricultural commodity markets and can increase households' exposure to crop price and yield shocks. At the same time, subsistence farming is also highly vulnerable to crop failures. In this work, we define household livelihood vulnerability as the probability of falling under an income threshold. We propose the use of a Bayesian network (BN) to calculate the income distribution based on household and community-level variables. BNs reflect relationships of dependence between variables and represent all variables as probability distributions, which allows for the explicit propagation of variability and uncertainty between variables. We focus on two agricultural frontier case study areas (CSAs) in northern Lao PDR that are at different stages in the transition from subsistence to export-oriented agriculture. Because agricultural production is the main livelihood activity in both CSAs, we develop a BN that calculates the probability distribution of net household agricultural production income. BN structure and parameterization are based on data collected in 110 household surveys across both CSAs, as well as interviews with villagers, government officials, and private sector actors. We analyze the effect of crop price and yield variability, land-use portfolio, and land holdings, on the probability of having a negative net agricultural income, which reflects a household's ability to meet its food consumption needs through cash crop sales. Results show that agricultural income is highly sensitive to rubber plantation area, rubber yield, and rubber price given the very large income potential of the crop. Households with larger agricultural areas have a lower probability of falling under an agricultural income threshold regardless of their diversification choices. Households that own more high-value cash crops are more buffered against rice yield shocks despite having higher agricultural income variability. However, low-income households are better off if they maintain a minimum level of rice sufficiency in combination with high-value cash crop production. Diversifying upland cash crops by increasing the share of cardamom (a lowvalue but low-volatility crop) at the expense of rubber (a highly lucrative crop with high price volatility) does not have a sizable beneficial impact, because returns from cardamom are significantly lower than for rubber. We show that BNs can be useful tools for the design and evaluation of rural development policies. ; ISSN:1708-3087

Hedonic house-price models have long been used in urban studies to investigate important factors characterizing cities (eg, the demand for amenities or housing submarkets). Traditionally, the formulation of hedonic models has been solved using global spatial econometric techniques. The development of local regression methods brought new insights into urban planning as the relationships between house prices and their determinants can be estimated locally and therefore mapped across space. Such maps provide planners and policy makers with valuable location-specific information to support their decision-making processes. A feature that is frequently overlooked when performing spatial local analysis is testing the statistical significance of local parameter estimates over space. This can be done by mapping the t-value of parameter estimates ( t-surfaces). In this study we propose the use of a mixed geographically weighted regression (mixed-GWR) technique to estimate a hedonic house-price model in Zurich. Mixed-GWR is an extended version of GWR by which some parameters are allowed to vary over space, while others remained fixed. To obtain spatially explicit results in a more meaningful way, we propose the use of t-surfaces to explore the statistical significance of selected local parameter estimates over space. We also follow the Bonferroni correction to overcome the problem of multiple hypothesis testing in local regression modelling. Results reveal interesting patterns in the spatial variability of local estimates for planners. For instance, areas are identified over which public policies such as house taxing have little or no effect on house pricing. Similarly, economic distortions in the housing market can be examined through the variability of residents' willingness to pay for larger dwellings. Also, urban development processes such as densification of cities can be supported by spatially exploring relevant socioeconomic variables.

Spatial planning seeks to regulate demand for land resources with a view to securing the well-being of urban and rural communities. It identifies the decisions that should be made in light of a preferred future development. Yet, preferences for future development change as demands for housing, recreation, food, and life styles are changing rapidly. In this study we aim to introduce a new approach for spatial planning, where the point of departure is not current data, but a future desired by stakeholders. To this end, we propose an inverse modeling approach where the result is a set of values for parameters identified as being key to reach a desired future. We apply the approach to a case study in a metropolitan area in Switzerland in order to illustrate its capabilities for sustainable planning. We invert a hedonic house-price model for identifying urban development options in the case-study area. We show how one can determine the relevant trade-offs between locational, structural, and socioeconomic characteristics given a desired house-price level, and the possible locations and relevant trade-offs for areas where future noise-emitting factories are to be planned. We discuss advantages and shortcomings of the approach for planners and draw conclusions about the effectiveness of the approach as a means of encouraging lay people and stakeholders to become involved efficiently in sustainable development issues.