What is a model, why people don't trust them, and why they should

It is easier to make one�s way in the world if one has some sort of expectation of the world�s future behaviour. Even when facing a very complex problem, we are rarely in a state of full ignorance: some expectations of system behaviour and the level of risk arising from uncertainty are usually available and it is on the basis of these expectations that most decisions are taken. Humans use models, which are mental or formal representations of reality, to generate these expectations, employing an ability that is shared more or less by all forms of life. Whether it is a tree responding to shortening day length by dropping its leaves and preparing its metabolism for the winter ahead or a naked Pleistocene ape storing food in advance of winter for the same reasons, both are using models. This view leads to two outcomes. The first is that predictions, seen as an expectation of ranges of future behaviours, are not just desirable, but necessary for decision-making. The often-asked question �do models provide reliable predictions?� then shifts to �given a certain problem, what type of models provide the most useful and reliable prediction?� The second outcome is that modelling is no longer a scientist�s activity but is instead a social process. Different types of models can be employed to ensure that all available information is included in model building and that model results are understood, trusted and acted upon

Atmospheric modeling of airborne GHG observations over Europe using a regional transport model: towards quantitative inversions using multiple species

Long-term observations of greenhouse gases are necessary to improve our understanding of sources and sinks of GHGs and their interaction with a changing climate. Such observations are used in combination with inverse atmospheric transport models to estimate surface-atmosphere exchange fluxes. Most of these observations are nowadays collected by ground-based networks of tall towers or satellites in low orbit. However, in the last decade, a new stream of data is gaining momentum: regularly collected airborne data. Airborne data provide an interesting alternative because by collecting observations along the vertical path of an aircraft it is possible to better understand the vertical structure of the atmosphere. Originally limited by the cost of rental aircrafts, this new source of data can now provide in-situ measurements on a regular basis thanks to strategic partnerships between academia and airlines all over the world. A clever way to reduce costs is in-fact to exploit platforms that are naturally bound to fly as much as possible like commercial airliners. In Europe, the leading project making use of this technique is MOZAIC/IAGOS (Measurements of Ozone by Airbus In-service airCraft / In-service Aircraft for a Global Observing System). The modeling framework used in this thesis combines a regional Lagrangian transport model (STILT, the Stochastic Time Inverted Lagrangian Transport model) with simulated fluxes from anthropogenic emissions for three trace gases (CO2, CO and CH4), biogenic emissions for CO2, and lateral boundary conditions from global models. We chose this framework because it ensures a fairly good representation of trace gas distribution, it allows for inverse modeling to retrieve regional fluxes, and is flexible enough to assess sources of mismatch between simulated and observed trace gas distributions

A hybrid multi-step approach for urban area mapping in the Province of Milan, Italy

AbstractRemote sensing products have proven an effective tool for the study of urban areas, from city management to environmental monitoring. This work focuses on the mapping of urban areas in the Province of Milan, Northern Italy, using mid-resolution remote sensing data covering the last 20 years. The methodology consists of three main steps: (i) a pixel-based classification tree, (ii) object-based filtering of agricultural terrain, and (iii) joining of land cover classes into two (urbanized and non-urbanized), adding post-classification editing. The final derived urban maps were validated and demonstrated to reach very good accuracy (error: 7–13%), thus providing reliable thematic information for urban planning of local and regional authorities

A simulation interface designed for improved user interaction and learning in water quality modelling software

Traditional simulation software that supports management decisions is configured and run by experienced scientists. However, it is often criticised for its lack of interactivity, not only in the application of decisions but also in the display of results. This paper presents the simulation interface of software with management strategy evaluation capabilities and its capacity to enable resource managers to learn about water quality management as evaluated in a workshop setting. The software ‘MSE Tool’ is not intended to produce definitive real-world advice but provides a test-bed for managers to interactively design strategies and explore the complexities inherent to water quality management using a simple, yet effective, user interface. MSE Tool has been used in a pilot application that simulated the effects of management strategies applied in catchments and their effects on riverine, estuarine and marine water quality in South East Queensland, Australia. The approach and the software are suitable for reuse in other management strategy evaluation projects

Assessing the Impact of Stakeholder Engagement in Management Strategy Evaluation

After completing a large, regional, multi-use Management Strategy Evaluation, we attempt to assess the impact of stakeholder engagement on the project. We do so by comparing the original project plan to the actual project development and highlight the changes which can be more directly related to stakeholder engagement aided by the application of a logic model for program evaluation. The impact can be summarised into four broad classes: a) change in the actual project development; b) a measurable change in the network of interactions both stakeholders (which includes researchers); c) changes in how the computer model was developed and run; and d) changes in attitudes among stakeholders (including researchers). We discuss these changes, the way they have been detected and some lessons we learnt which may benefit future Management Strategy Evaluation projects

Development and validation of a scoring system for pre-surgical and early post-surgical prediction of bariatric surgery unsuccess at 2 years

Bariatric surgery (BS) is an effective treatment for morbid obesity. However, a simple and easy-to-use tool for the prediction of BS unsuccess is still lacking. Baseline and follow-up data from 300 consecutive patients who underwent BS were retrospectively collected. Supervised regression and machine-learning techniques were used for model development, in which BS unsuccess at 2 years was defined as a percentage of excess-weight-loss (%EWL) < 50%. Model performances were also assessed considering the percentage of total-weight-loss (%TWL) as the reference parameter. Two scoring systems (NAG-score and ENAG-score) were developed. NAG-score, comprising only pre-surgical data, was structured on a 4.5-point-scale (2 points for neck circumference ≥ 44 cm, 1.5 for age ≥ 50 years, and 1 for fasting glucose ≥ 118 mg/dL). ENAG-score, including also early post-operative data, was structured on a 7-point-scale (3 points for %EWL at 6 months ≤ 45%, 1.5 for neck circumference ≥ 44 cm, 1 for age ≥ 50 years, and 1.5 for fasting glucose ≥ 118 mg/dL). A 3-class-clustering was proposed for clinical application. In conclusion, our study proposed two scoring systems for pre-surgical and early post-surgical prediction of 2-year BS weight-loss, which may be useful to guide the pre-operative assessment, the appropriate balance of patients’ expectations, and the post-operative care

On the representation of IAGOS/MOZAIC vertical profiles in chemical transport models:contribution of different error sources in the example of carbon monoxide

Utilising a fleet of commercial airliners, MOZAIC/IAGOS provides atmospheric composition data on a regular basis that are widely used for modelling applications. Due to the specific operational context of the platforms, such observations are collected close to international airports and hence in an environment characterised by high anthropogenic emissions. This provides opportunities for assessing emission inventories of major metropolitan areas around the world, but also challenges in representing the observations in typical chemical transport models. We assess here the contribution of different sources of error to overall modeldata mismatch using the example of MOZAIC/IAGOS carbon monoxide (CO) profiles collected over the European regional domain in a time window of 5 yr (20062011). The different sources of error addressed in the present study are: 1) mismatch in modelled and observed mixed layer height; 2) bias in emission fluxes and 3) spatial representation error (related to unresolved spatial variations in emissions). The modelling framework combines a regional Lagrangian transport model (STILT) with EDGARv4.3 emission inventory and lateral boundary conditions from the MACC reanalysis. The representation error was derived by coupling STILT with emission fluxes aggregated to different spatial resolutions. We also use the MACC reanalysis to assess uncertainty related to uncertainty sources 2) and 3). We treat the random and the bias components of the uncertainty separately and found that 1) and 3) have a comparable impact on the random component for both models, while 2) is far less important. On the other hand, the bias component shows comparable impacts from each source of uncertainty, despite both models being affected by a low bias of a factor of 22.5 in the emission fluxes. In addition, we suggested methods to correct for biases in emission fluxes and in mixing heights. Lastly, the evaluation of the spatial representation error against modeldata mismatch between MOZAIC/IAGOS observations and the MACC reanalysis revealed that the representation error accounts for roughly 1520% of the modeldata mismatch uncertainty

Measurements of Soil Carbon Dioxide Emissions from Two Maize Agroecosystems at Harvest under Different Tillage Conditions

In this study a comparison of the soil CO2 fluxes emitted from two maize (Zea mays L.) fields with the same soil type was performed. Each field was treated with a different tillage technique: conventional tillage (30\u2009cm depth ploughing) and no-tillage. Measurements were performed in the Po Valley (Italy) from September to October 2012, covering both pre- and postharvesting conditions, by means of two identical systems based on automatic static soil chambers. Main results show that no-tillage technique caused higher CO2 emissions than conventional tillage (on average 2.78 and 0.79\u2009\u3bcmol CO2\u2009m 122\u2009s 121, resp.). This result is likely due to decomposition of the organic litter left on the ground of the no-tillage site and thus to an increased microbial and invertebrate respiration. On the other hand, fuel consumption of conventional tillage technique is greater than no-tillage consumptions. For these reasons this result cannot be taken as general. More investigations are needed to take into account all the emissions related to the field management cycle