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The presented article will describe an 'effect-cause' model for the purpose of simulating the energy consumption of DC fed light rail vehicles. The model will assess the advantages of hybrid vehicles in terms of energy consumption, network power and voltage variations, line current and losses; and will help sizing and designing a supercapacitor based energy storage system (ESS) for both on-board, and stationary applications. The proposed modeling needs to allow the ESS sizing according to the objective that needs to be achieved, being braking energy recovery, voltage drop compensation and peak power shaving the most common goals of ESS use in hybrid vehicles. The needed power and energy levels will vary in function of the vehicle features and the driving cycle followed. This all can be determined by the quasi-static simulation tool to ease the design process. Another objective of the modeling tool is to evaluate the behaviour of the vehicle power flow controller, which manages the power from/to the ESS in function of the state of several variables.

The presented article will describe an 'effect-cause' model for the purpose of simulating the energy consumption of DC fed light rail vehicles. The model will assess the advantages of hybrid vehicles in terms of energy consumption, network power and voltage variations, line current and losses; and will help sizing and designing a supercapacitor based energy storage system (ESS) for both on-board, and stationary applications. The proposed modeling needs to allow the ESS sizing according to the objective that needs to be achieved, being braking energy recovery, voltage drop compensation and peak power shaving the most common goals of ESS use in hybrid vehicles. The needed power and energy levels will vary in function of the vehicle features and the driving cycle followed. This all can be determined by the quasi-static simulation tool to ease the design process. Another objective of the modeling tool is to evaluate the behaviour of the vehicle power flow controller, which manages the power from/to the ESS in function of the state of several variables.

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979-2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04m3m-3, respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007-2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates.

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979-2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04m3m-3, respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007-2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates.

Life Cycle Assessment is a tool to assess the environmental impacts and resources used throughout a product's life cycle, i.e., from raw material acquisition, via production and use phases, to waste management. The methodological development in LCA has been strong, and LCA is broadly applied in practice. The aim of this paper is to provide a review of recent developments of LCA methods. The focus is on some areas where there has been an intense methodological development during the last years. We also highlight some of the emerging issues. In relation to the Goal and Scope definition we especially discuss the distinction between attributional and consequential LCA. For the Inventory Analysis, this distinction is relevant when discussing system boundaries, data collection, and allocation. Also highlighted are developments concerning databases and Input-Output and hybrid LCA. In the sections on Life Cycle Impact Assessment we discuss the characteristics of the modelling as well as some recent developments for specific impact categories and weighting. In relation to the Interpretation the focus is on uncertainty analysis. Finally, we discuss recent developments in relation to some of the strengths and weaknesses of LCA.

Life Cycle Assessment is a tool to assess the environmental impacts and resources used throughout a product's life cycle, i.e., from raw material acquisition, via production and use phases, to waste management. The methodological development in LCA has been strong, and LCA is broadly applied in practice. The aim of this paper is to provide a review of recent developments of LCA methods. The focus is on some areas where there has been an intense methodological development during the last years. We also highlight some of the emerging issues. In relation to the Goal and Scope definition we especially discuss the distinction between attributional and consequential LCA. For the Inventory Analysis, this distinction is relevant when discussing system boundaries, data collection, and allocation. Also highlighted are developments concerning databases and Input-Output and hybrid LCA. In the sections on Life Cycle Impact Assessment we discuss the characteristics of the modelling as well as some recent developments for specific impact categories and weighting. In relation to the Interpretation the focus is on uncertainty analysis. Finally, we discuss recent developments in relation to some of the strengths and weaknesses of LCA.