In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km2). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments. 相似文献
This article proposes a fundamental methodological shift in the modelling of policy interventions for sustainability transitions in order to account for complexity (e.g. self-reinforcing mechanisms, such as technology lock-ins, arising from multi-agent interactions) and agent heterogeneity (e.g. differences in consumer and investment behaviour arising from income stratification). We first characterise the uncertainty faced by climate policy-makers and its implications for investment decision-makers. We then identify five shortcomings in the equilibrium and optimisation-based approaches most frequently used to inform sustainability policy: (i) their normative, optimisation-based nature, (ii) their unrealistic reliance on the full-rationality of agents, (iii) their inability to account for mutual influences among agents (multi-agent interactions) and capture related self-reinforcing (positive feedback) processes, (iv) their inability to represent multiple solutions and path-dependency, and (v) their inability to properly account for agent heterogeneity. The aim of this article is to introduce an alternative modelling approach based on complexity dynamics and agent heterogeneity, and explore its use in four key areas of sustainability policy, namely (1) technology adoption and diffusion, (2) macroeconomic impacts of low-carbon policies, (3) interactions between the socio-economic system and the natural environment, and (4) the anticipation of policy outcomes. The practical relevance of the proposed methodology is subsequently discussed by reference to four specific applications relating to each of the above areas: the diffusion of transport technology, the impact of low-carbon investment on income and employment, the management of cascading uncertainties, and the cross-sectoral impact of biofuels policies. In conclusion, the article calls for a fundamental methodological shift aligning the modelling of the socio-economic system with that of the climatic system, for a combined and realistic understanding of the impact of sustainability policies. 相似文献
Four policies might close the gap between the global GHG emissions expected for 2020 on the basis of current (2013) policies and the reduced emissions that will be needed if the long-term global temperature increase can be kept below the 2 °C internationally agreed limit. The four policies are (1) specific energy efficiency measures, (2) closure of the least-efficient coal-fired power plants, (3) minimizing methane emissions from upstream oil and gas production, and (4) accelerating the (partial) phase-out of subsidies to fossil-fuel consumption. In this article we test the hypothesis of the International Energy Agency (IEA) that these policies will not result in a loss of gross domestic product (GDP) and we estimate their employment effects using the E3MG global macro-econometric model. Using a set of scenarios we assess each policy individually and then consider the outcomes if all four policies were implemented simultaneously. We find that the policies are insufficient to close the emissions gap, with an overall emission reduction that is 30% less than that found by the IEA. World GDP is 0.5% higher in 2020, with about 6 million net jobs created by 2020 and unemployment reduced.
Policy relevance
The gap between GHG emissions expected under the Copenhagen and Cancun Agreements and that needed for emissions trajectories to have a reasonable chance of reaching the 2 °C target requires additional policies if it is to be closed. This article uses a global simulation model E3MG to analyse a set of policies proposed by the IEA to close the gap and assesses their macroeconomic effects as well as their feasibility in closing the gap. It complements the IEA assessment by estimating the GDP and employment implications separately by the different policies year by year to 2020, by major industries, and by 21 world regions. 相似文献