Júlia Seixas

Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval

We analyze the impacts of the steady state assumption on inverse model parameter retrieval from biogeochemical models. An inverse model parameterization study using eddy covariance CO 2 flux data was performed with the Carnegie Ames Stanford Approach (CASA) model under conditions of strict and relaxed carbon cycle steady state assumption (CCSSA) in order to evaluate both the robustness of the models structure for the simulation of net ecosystem carbon fluxes and the assessment of the CCSSA effects on simulations and parameter estimation. Net ecosystem production (NEP) measurements from several eddy covariance sites were compared with NEP estimates from the CASA model driven by local weather station climate inputs as well as by remotely sensed fraction of photosynthetically active radiation absorbed by vegetation and leaf area index. The parameters considered for optimization are directly related to aboveground and belowground modeled responses to temperature and water availability, as well as a parameter (η) that relaxed the CCSSA in the model, allowing for site level simulations to be initialized either as net sinks or sources. A robust relationship was observed between NEP observations and predictions for most of the sites through the range of temporal scales considered (daily, weekly, biweekly, and monthly), supporting the conclusion that the model structure is able to capture the main processes explaining NEP variability. Overall, relaxing CCSSA increased model efficiency (21%) and decreased normalized average error (-92%). Intersite variability was a major source of variance in model performance differences between fixed (CCSSA f ) and relaxed (CCSSA r ) CCSSA conditions. These differences were correlated with mean annual NEP observations, where an average increase in modeling efficiency of 0.06 per 100 g Cm -2 a -1 (where a is years) of NEP is observed (α < 0.003). The parameter η was found to be a key parameter in the optimization exercise, generating significant model efficiency losses when removed from the initial parameter set and parameter uncertainties were significantly lower under CCSSA r . Moreover, modeled soil carbon stocks were generally closer to observations once the steady state assumption was relaxed. Finally, we also show that estimates of individual parameters are affected by the steady state assumption. For example, estimates of radiation-use efficiency were strongly affected by the CCSSA f indicating compensation effects for the inadequate steady state assumption, leading to effective and thus biased parameters. Overall, the importance of model structural evaluation in data assimilation approaches is thus emphasized.

Anopheles atroparvus density modeling using MODIS NDVI in a former malarious area in Portugal.

ABSTRACT: Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the models ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities.

Top-down and bottom-up modelling to support low-carbon scenarios: climate policy implications

Bottom-up and top-down models are used to support climate policies, to identify the options required to meet GHG abatement targets and to evaluate their economic impact. Some studies have shown that the GHG mitigation options provided by economic top-down and technological bottom-up models tend to vary. One reason for this is that these models tend to use different baseline scenarios. The bottom-up TIMES_PT and the top-down computable general equilibrium GEM-E3_PT models are examined using a common baseline scenario to calibrate them, and the extend of their different mitigation options and its relevant to domestic policy making are assessed. Three low-carbon scenarios for Portugal until 2050 are generated, each with different GHG reduction targets. Both models suggest close mitigation options and locate the largest mitigation potential to energy supply. However, the models suggest different mitigation options for the end-use sectors: GEM-E3_PT focuses more on energy efficiency, while TIMES_PT relies on decrease carbon intensity due to a shift to electricity. Although a common baseline scenario cannot be ignored, the models’ inherent characteristics are the main factor for the different outcomes, thereby highlighting different mitigation options. Policy relevance The relevance of modelling tools used to support the design of domestic climate policies is assessed by evaluating the mitigation options suggested by a bottom-up and a top-down model. The different outcomes of each model are significant for climate policy design since each suggest different mitigation options like end-use energy efficiency and the promotion of low-carbon technologies. Policy makers should carefully select the modelling tool used to support their policies. The specific modelling structures of each model make them more appropriate to address certain policy questions than others. Using both modelling approaches for policy support can therefore bring added value and result in more robust climate policy design. Although the results are specific for Portugal, the insights provided by the analysis of both models can be extended to, and used in the climate policy decisions of, other countries.

Fragmentation patterns of evergreen oak woodlands in Southwestern Iberia: Identifying key spatial indicators

Mediterranean evergreen oak woodlands (composed of Quercus suber L. and Quercus rotundifolia Lam.) are becoming increasingly fragmented in the human-modified landscapes of Southwestern Portugal and Spain. Previous studies have largely neglected to assess the spatial changes of oak woodlands in relation to their surrounding landscape matrix, and to characterize and quantify woodland boundaries and edges. The present study aims to fill this gap by analyzing fragmentation patterns of oak woodlands over a 50-year period (1958-2007) in three landscapes. Using archived aerial imagery from 1958, 1995 and 2007, for two consecutive periods (1958-1995 and 1995-2007), we calculated a set of landscape metrics to compare woodland fragmentation over time. Our results indicated a continuous woodland fragmentation characterized by their edge dynamics. From 1958 to 2007, the replacement of open farmland by shrubland and by new afforestation areas in the oak woodland landscape surrounding matrix, led to the highest values for edge contrast length trends of 5.0 and 12.3, respectively. Linear discriminant analysis was performed to delineate fragmented woodland structures and identify metric variables that characterize woodland spatial configuration. The edge contrast length with open farmland showed a strong correlation with F1 (correlations ranging between 0.55 and 0.98) and may be used as a proxy for oak woodland mixedness in landscape matrix. The edge dynamics of oak woodlands may result in different patterns of oak recruitment and therefore, its study may be helpful in highlighting future baselines for the sustainable management of oak woodlands.

Renewable energy sources availability under climate change scenarios — Impacts on the Portuguese energy system

This paper presents an assessment of the impacts on the Portuguese energy system due to climate change induced water availability variations. Two different scenarios were evaluated on the 2050 time horizon: strong and weak decrease of water availability compared to a reference scenario. The impact of water availability decrease, in particular for the power sector, was assessed using a bottom-up technology based linear optimization model: TIMES (The Integrated Markal-EFOM System) calibrated and validated for Portugal. Results indicate that currently planned hydropower capacity is highly overestimated. Results also suggest that under strong decrease of water availability, marginal CO2 abatement costs in 2050 are doubled for moderate reductions targets but, as restrictions are tightened, different scenarios of water availability have little impact on the marginal CO2 abatement costs.

Smart City Energy Planning: Integrating Data and Tools

This paper presents an innovative analytical framework to address incomplete interpretations and dispersed data of the energy system in cities, which usually generate multiple inefficiencies. Integrative city planning takes the city energy system from the supply to the demand while considering its spatial representativeness, and drives optimal cost-efficient assessment towards future sustainable energy targets. This holistic approach delivers more adequate policies and measures towards higher energy use efficiency. The proposed analytical framework has been developed within the INSMART EU funded project and focuses on data gathering procedures and data processing tools and models, covering a wide range of citys energy consumers, as residential buildings, transport and utilities. The results, mapped into a GIS, can be further exploited either for awareness increase of citizens and for decision support of city energy planners.

Energy Policies Influenced by Energy Systems Modelling—Case Studies in UK, Ireland, Portugal and G8

A key objective of IEA-ETSAP is to assist decision makers in robustly developing, implementing and assessing the impact of energy and climate mitigation policies. This chapter focuses on four case studies, in which there is clear evidence of a direct link between the use of MARKAL and TIMES scenario modelling activities and the resulting policy decisions. The case studies selected assess how the (i) UK MARKAL model informed the development of energy and climate mitigation policy in the UK, focusing on the Energy White Paper in 2003, the Energy White Paper in 2007 and the Climate Change Act in 2008; (ii) Irish TIMES model informed the development of climate mitigation legislation in Ireland in 2014 and Ireland’s negotiating position regarding the EU 2030 Climate Energy Package in 2014; (iii) TIMES_PT model informed climate policy in Portugal in the last 10 years and has supported the design of climate mitigation policies; (iv) IEA ETP Model informed the G8 in responding to the 2005 Gleneagles Plan of Action and has supported the work of the Major Economies Forum and Clean Energy Ministerial. This chapter collates methodologies and results from these different case studies and summarizes some key findings regarding (i) policy frameworks and goals; (ii) how policy makers have been intertwined with the modelling tool during the modelling process; (iii) the role of the economic stakeholders dialogue; (iv) main insights from the modelling exercises; (v) lessons learnt: from effective contributions to real limitations and (vi) recommendations.

Long term energy scenarios under uncertainty

Traditionally a set of equally feasible scenarios is built to represent possible future energy outcomes. However, current approaches do not handle properly different scenarios appointing to divergent situations. This paper illustrates the fragilities of the state-of-the-art deterministic long term energy scenarios approach through the study of uncertain scenarios for fossil fuel prices and energy demand, using TIMES model for Portugal. Results show quite divergent power sector profiles and divergent investments options, when oil prices or demand for useful energy changes. Regarding the relevance of energy decision making under uncertainty, the paper proposes a methodology to deal with long term energy scenarios under uncertain parameters evolution. The methodology assumes a stochastic approach and connects a top-down and bottom-up model. The linkage is made through a calibration process based on iterative simulations, combining top-down properties with a bottom-up outcome.

A Global Renewable Energy Roadmap: Comparing Energy Systems Models with IRENA’s REmap 2030 Project

In 2014, the International Renewable Energy Agency (IRENA) published a global renewable energy roadmap—called REmap 2030—to double the share of renewables in the global energy mix by 2030 compared to 2010 (IRENA, A Renewable Energy Roadmap, 2014a). A REmap tool was developed to facilitate a transparent and open framework to aggregate the national renewable energy plans and/or scenarios of 26 countries. Unlike the energy systems models by IEA-ETSAP teams, however, the REmap tool does not account for trade-offs between renewable energy and energy efficiency activities, system planning issues like path dependency and investments in the grid infrastructure, competition for scarce resources—e.g. biomass—in the commodity prices, or dynamic cost developments as technologies get deployed over time. This chapter compares the REmap tool with the IEA-ETSAP models at two levels: the results and the insights. Based on the results comparison, it can be concluded that the REmap tool can be used as a way to explicitly engage national experts, to scope renewable energy options, and to compare results across countries. However, the ETSAP models provide detailed insights into the infrastructure requirements, competition between technologies and resources, and the role of energy efficiency needed for planning purposes. These insights are particularly relevant for countries with infrastructure constraints and/or ambitious renewable energy targets. As more and more countries are turning to renewables to secure their energy future, the REmap tool and the ETSAP models have complementary roles to play in engaging policy makers and national energy planners to advance renewables.

Air Pollution Space-Time Analysis

The environmental decision-making process is preceded by data analysis, which is conditioned by a specific temporal and spatial scale. This paper aims to present a methodology that integrates the time-space framework of air quality data to infer the temporal pattern and spatial variability that could be interpreted for environmental decision purposes. Variograms that accommodate time and space lags were used for the analysis and proved to be effective. Temporal and spatial trends were found for data collected on an hourly and daily basis and its environmental meaning is discussed. Visualization of spatial patches of air pollution in Lisbon during a working day is performed through the use of an image processing technique, named morphing.