The representation of physical processes by a climate model depends on its structure, numerical schemes, physical parameterizations and resolution, with initial conditions and future emission scenarios further affecting the output. The extent to which climate models agree is therefore of great interest, often with greater confidence in robust results across models. This has led to climate model output being analysed as ensembles rather than in isolation, and quantifying the sources of variation across these ensembles is the aim of many recent studies. Statistical attempts to do this include the use of variants of the mixed-effects analysis of variance or covariance (mixed-effects ANOVA/ANCOVA). This work usually focuses on identifying variation in a variable of interest that is due to differences in model structure, carbon emissions scenario, etc. Quantifying such variation is important in determining where models agree or disagree, but further statistical approaches can be used to diagnose the reasons behind the agreements and disagreements by representing the physical processes within the climate models. A process-based approach is presented that uses simulation with statistical models to perform a global sensitivity analysis and quantify the sources of variation in multi-model ensembles. This approach is a general framework that can be used with any generalised linear mixed model (GLMM), which makes it applicable to use with statistical models designed to represent (sometimes complex) physical relationships within different climate models. The method decomposes the variation in the response variable into variation due to 1) temporal variation in the driving variables, 2) variation across ensemble members in the distributions of the driving variables, 3) variation across ensemble members in the relationship between the response and the driving variables, and 4) variation unexplained by the driving variables. The method is used to quantify the extent to which, and diagnose why, precipitation varies across and within the members of two different climate model ensembles on various different spatial and temporal scales. Change in temperature in response to increased CO2 is related to change in global-mean annual-mean precipitation in a multi-model ensemble of general circulation models (GCMs). A total of 46% of the variation in the change in precipitation in the ensemble is found to be due to the differences between the GCMs, largely because the distribution of the changes in temperature varies greatly across different GCMs. The total variation in the annual-mean change in precipitation that is due to the differences between the GCMs depends on the area over which the precipitation is averaged, and can be as high as 63%. The second climate model ensemble is a perturbed physics ensemble using a regional climate model (RCM). This ensemble is used for three different applications. Firstly, by using lapse rate, saturation specific humidity and relative humidity as drivers of daily-total summer convective precipitation at the grid-point level over southern Britain, up to 8% of the variation in the convective precipitation is found to be due to the uncertainty in RCM parameters. This is largely because given atmospheric conditions lead to different rates of precipitation in different ensemble members. This could not be detected by analysing only the variation across the ensemble members in mean precipitation rate (precipitation bias). Secondly, summer-total precipitation at the grid-point level over the British Isles is used to show how the values of the RCM parameters can be incorporated into a GLMM to quantify the variation in precipitation due to perturbing each individual RCM parameter. Substantial spatial variation is found in the effect on precipitation of perturbing different RCM parameters. Thirdly, the method is extended to focus on extreme events, and the simulation of extreme winter pentad (five-day mean) precipitation events averaged over the British Isles is found to be robust to the uncertainty in RCM parameters.

Process-based models are used in a diverse array of fields, including environmental engineering to provide supporting information to engineers, policymakers and stakeholdes. Recent advances in remote sensing and data storage technology have provided opportunities for improving the application of process-based models and visualizing data, but also present new challenges. The availability of larger quantities of data may allow models to be constructed and calibrated in a more thorough and precise manner, but depending on the type and volume of data, it is not always clear how to incorporate the information content of these data into a coherent modeling framework. In this context, using process-based models in new ways to provide decision support or to produce more complete and flexible predictive tools is a key task in the modern data-rich engineering world. In standard usage, models can be used for simulating specific scenarios; they can also be used as part of an automated design optimization algorithm to provide decision support or in a data-assimilation framework to incorporate the information content of ongoing measurements. In that vein, this thesis presents and demonstrates extensions and refinements to leverage the best of what process-based models offer using Differential Evolution (DE) the Extended Kalman Filter (EKF). Coupling multi-objective optimization to a process-based model may provide valuable information provided an objective function is constructed appropriately to reflect the multi-objective problem and constraints. That, in turn, requires weighting two or more competing objectives in the early stages of an analysis. The methodology proposed here relaxes that requirement by framing the model optimization as a sensitivity analysis. For demonstration, this is implemented using a surface water model (HEC-RAS) and the impact of floodplain access up and downstream of a fixed bridge on bridge scour is analyzed. DE, an evoutionary global optimization algorithm, is wrapped around a calibrated HEC-RAS model. Multiple objective functions, representing different relative weighting of two objectives, are used; the resulting rank-orders of river reach locations by floodplain access sensitivity are consistent across these multiple functions. To extend the applicability of data assimilation methods, this thesis proposes relaxing the requirement that the model be calibrated (provided the parameters are still within physically defensible ranges) before performing assimilation. The model is then dynamically calibrated to new state estimates, which depend on the behavior of the model. Feasibility is demonstrated using the EKF and a synthetic dataset of pendulum motion. The dynamic calibration method reduces the variance of prediction errors compared to measurement errors using an initially uncalibrated model and produces estimates of calibration parameters that converge to the true values. The potential application of the dynamic calibration method to river sediment transport modeling is proposed in detail, including a method for automated calibration using sediment grain size distribution as a calibration parameter.

Carbon (C) pools and fluxes in northern hemisphere forest ecosystems are attracting increasing attention concerning predicted climate change. This thesis studied C fluxes, particularly soil C dynamics, in spruce forest ecosystems in relation to interactions between physical/biological processes using a process-based ecosystem model (CoupModel) with data for Swedish conditions. The model successfully described general patterns of C and N dynamics in managed spruce forest ecosystems with both tree and field layers. Using regional soil and plant data, the change in current soil C pools was -3 g C m-2 yr-1 in northern Sweden and +24 g C m-2 yr-1 in southern Sweden. Simulated climate change scenarios resulted in increased inflows of 16-38 g C m-2 yr-1 to forest ecosystems throughout Sweden, with the highest increase in the south and the lowest in the north. Along a north-south transect, this increased C sequestration mainly related to increased tree growth, as there were only minor decreases in soil C pools. Measurements at one northern site during 2001-2002 indicated large soil C losses (-96 g C m-2 yr-1), which the model successfully described. However, the discrepancy between these large losses and substantially smaller losses obtained in regional simulations was not explained. A simulation based on Bayesian calibration successfully reproduced measured C, water and energy fluxes, with estimated uncertainties for major components of the simulated C budget. Site-specific measurements indicated a large contribution from field layer fine roots to total litter production, particularly in northern Sweden. Mean annual tree litter production was 66% higher at the most southerly site (240 g C m-2 yr-1 compared with 145 g C m-2 yr-1 in the north), but when field and bottom layers were included the difference decreased to 16% (total litter production 276 g C m-2 yr-1 and 239 g C m-2 yr-1 respectively). Regional simulations showed that decomposition rate for the stable soil C fraction was three times higher in northern regions compared with southern, providing a possible explanation why soil C pools in southern Sweden are roughly twice as large as those in the north. / QC 20100922

Under de senaste åren har belastningen på akutsjukvården ökat, akutmottagningarna har försökt genom organisatoriska förändringar förkorta väntetiderna för patienter som behöver akutsjukvård. Socialstyrelsen har krävt att landets sjukhus inför så kallade fast track eller snabbspår. Fast track innebär att patienterna passerar förbi akutmottagningar och får en mer påskyndad undersökning och behandling. Därför har man infört en systematisk prioritering av patientgrupper med avgränsade och relativt lättdiagnostiserade åkommor. Behovet av förändringar i vården är mer tydligt i dag än tidigare. Kravet berör SOS alarm, ambulanssjukvården, den primära och kommunala vården och kräver ett snabbt samt effektivt samarbete. Tiden för diagnos och behandling förväntas där igenom bli kortare. Syftet med studien är att beskriva ambulanssjuksköterskans upplevelse vid användandet av fastställda behandlingsriktlinjer för fast track patienter.  Studien baseras på kvalitativa intervjuer och efter genomförd innehållsanalys framkom fyra huvudkategorier. Resultatet visar att fastställda behandlingsriktlinjer skapar trygghet hos ambulanssjuksköterskan men vissa fast track kan skapa otrygghet på grund av otydlighet i behandlingsriktlinjerna, att logistiken mellan berörda enheter inte alltid fungerar vilket upplevs frustrerande och upplevelsen av utebliven utvärdering samt att inte kunna påverka behandlingsriktlinjerna. Diskussionen bygger på vinsterna både för patienten och organisationen med processbaserat arbetssätt men även på tillgången och efterfrågan på ambulanssjukvårdens resurser som kan ge effekter på ambulanssjuksköterskans arbetsmiljö.

Extensive research has been undertaken to improve the robustness of runoff quantity predictions for urban catchments. However, equally robust predictions for runoff quality have yet to be attained. Past studies addressing this issue have typically been confined to the use of simple conceptual or empirical models which forgo the tedious steps of providing a physical representation of the actual system to be modelled. Consequently, even if the modelling results for the test catchments are satisfactory, the reliability and applicability of these models for other catchments remain uncertain. It is deemed that by employing process-based, deterministic models, many of these uncertainties can be eliminated. A lack of understanding of the hydrological processes occurring during storm events and the absence of good calibration data, however, hamper the advancement of such models and limit their use in the field. This research proposes that the development of a hydrologic model based on the kinematic wave equations linked to an advection-dispersion model that simulates pollutant detachment and transport will improve both runoff quantity and quality simulations and enhance the robustness of the predictions. At the very worst, a model of this type could still highlight the underlying issues that inhibit models from reproducing the recorded historical hydrographs and pollutographs. In actual fact, this approach has already been applied by various modellers to simulate the entrainment of pollutants from urban catchments. Also, the paradigm shift to using the Water Sensitive Urban Design (WSUD) approach in designing urban stormwater systems has prompted the need to differentiate the various sources of pollutants in urban catchments such as roads, roofs and other impervious surfaces. The primary objective of the study reported herein is to model runoff quantity and quality from small urban catchments, facilitated by the procurement of the necessary field data to calibrate and validate the model via implementation of a comprehensive field exercise based in Sydney. From a water quality perspective, trace metals were selected as the foci. The study outcomes include the formulation of a linkage of models capable of providing accurate and reliable runoff quantity and quality predictions for the study catchments by taking into consideration: - The different availability of pollutants from urban catchments, i.e. roads vs. roofs; - The build-up characteristics of pollutants on the distinct urban surfaces and their spatial distribution; - The contribution of rainwater to urban runoff pollution; - The partitioning of pollutants according to particulate bound and dissolved phases; - The respective role of rainfall and runoff in the detachment and entrainment of pollutants; - The influence of particle properties such as particle size distribution and density on pollutant transport; and - The relationship associating particulate bound metals to suspended solids. The simulation results obtained using the proposed model were found to be suitable for modelling the detachment and transport of pollutants for small urban catchments. Interpretation of these results reveals several key findings which could help to rectify shortcomings of existing modelling approaches. Even though the robustness of the model presented here may not translate into a significant improvement in the overall robustness of model predictions, the physical basis on which this process-based model was developed nevertheless provides the flexibility necessary for implementation at alternative sites. It is also shown that the availability of reliable runoff data is essential for implementation of the model for other similar urban catchments. In conclusion, the proposed model in this study will serve as a worthy tool in future urban catchment management studies.

This research involved a mixed methods exploration of barriers encountered by adult learners in university, process-based learning courses, and strategies used to address these barriers. Multiple perspectives included the learners, their families, professors and administrators. Data enabled expansion of Cross' Chain-of-Response model and provided knowledge for university administrators to improve practice.

Process-based arguments aim at demonstrating that a process, compliant with a standard, has been followed during the development of a safety-critical system. Compliance with these processes is mandatory for certification purposes, so the generation of process-based arguments is essential, but also a very costly and time-consuming task. In addition, inappropriate reasoning in the argumentation such as insufficient evidence (i.e. a fallacious argumentation), may result in a loss of quality of the system, leading to safety-related failures. Therefore, avoiding or detecting fallacies in process-based arguments is crucial. However, the process of reviewing such arguments is currently done manually and is based on the expert’s knowledge, so it is a very laborious and error-prone task.In this thesis, an approach to automatically generate fallacy-free process-based arguments is proposed and implemented. This solution is composed of two parts; (i) detecting omission of key evidence fallacies on the modelled processes, and (ii) transforming them into process-based safety arguments. The former checks automatically if the process model, compliant with the Software & Systems Process Engineering Metamodel (SPEM) 2.0, contains the sufficient information for not committing an omission of key evidence fallacy. If fallacies are detected, the functionality provides the proper recommendation to resolve them. Once the safety engineers/process engineers modify the process model following the provided recommendations, the second part of the solution can be applied. This one generates automatically the process-based argument, compliant with the Structured Assurance Case Metamodel (SACM), and displays it –rendered via Goal Structuring Notation (GSN)– into the OpenCert assurance case editor within the AMASS platform. The applicability of the solution is validated in the context of the ECSS-E-ST-40C standard.

Orientador: José Luiz Stape / Resumo: O Brasil é um dos líderes mundiais na produção de celulose, papel e painéis de madeira. Este sucesso se deve a diversos fatores, tais como: às condições edafoclimáticas favoráveis ao desenvolvimento de espécies de alto rendimento; aos investimentos nos setores de pesquisa e inovação; ao estabelecimento de parcerias entre empresas e instituições de pesquisa e ensino. Desde a chegada das primeiras árvores de eucalipto, no início do século XX, a produtividade da espécie e a quantidade de área plantada cresceram no país, por outro lado, a busca por melhorias deve ser constante. O uso de modelos baseados em processos tem sido direcionado à compreensão da influência de fatores edafoclimáticos em parâmetros da espécie. Desta forma, é possível simular ambientes em situações adversas e prever possíveis impactos das mudanças climáticas na produtividade da espécie, assim como selecionar áreas aptas à cultura, além de determinar fatores que limitam o crescimento. Os objetivos deste trabalho foram parametrizar e validar o modelo 3-PG para o clone de eucalipto de maior plasticidade fenotípica para diferentes regiões do Brasil; e estimar a produtividade deste mesmo clone para diferentes ciclos climáticos em uma região onde a setor de florestas plantadas ainda não está estabelecido, como o estado de Rondônia. A parametrização do modelo foi eficiente quanto a predição de produção de biomassa de lenho (R2 = 0,93), diâmetro a altura do peito (R2 = 0,94) e área basal (R2 = 0,93), o mesmo não aco... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Brazil is one of the world leaders in the production of pulp, paper and wood panels. This success is due to several factors, such as: the edaphoclimatic conditions favorable to the development of high yield species; the investments in research and innovation sectors; the establishment of partnerships between companies, research and education institutions. Since the arrival of the first Eucalyptus trees at the beginning of the 20th century, the productivity of the species and the amount of planted area have grown in the country, in addition, the search for improvement must be constant. Technologies related to remote sensing, genetic sequencing, genetically modified organisms, ecophysiology and modeling of forest systems enable the filling of previously unknown gaps. The use of process-based models has been directed at understanding the influence of edafoclimatic factors on species parameters. In this way, it is possible to simulate environments in adverse situations and predict possible impacts of climate change on the productivity of the species, as well as to select areas suitable for cultivation, and to determine factors that limit growth. The objectives of this study were to parameterize and validate the 3-PG model for the Eucalyptus clone of higher phenotypic plasticity for different regions of Brazil; and to estimate the productivity of this same clone for different climatic cycles in a region where the planted forest sector is not yet established, state of Rondônia. The... (Complete abstract click electronic access below) / Doutor

Herbage and cattle production in semi-arid regions are primarily controlled by climate variation particularly rainfall variability and secondarily by disturbances such as drought, grazing and fire. These factors interact at different spatial and temporal scales in a complex manner difficult to observe or comprehend and, reduce availability and quality of herbage and cattle productivity. Variables for quantifying rangeland productivity are thus rarely available and unreliable yet options for sustainable management are limited. Grazing experiments have provided useful insight about ecological and management factors involved in rangeland functioning, but they have limited scope to deal with high environmental variation. This highlights the need for a systems approach for monitoring rangeland and cattle productivity at the appropriate spatial and temporal scales to enable productivity to be maximised whilst risk to climate variation is minimised. This study explored two broad objectives: to determine the ranch-scale impacts of rainfall variability and drought on herbaceous aboveground biomass (AGB) using optical remote sensing; and to parameterise, evaluate and apply a systems model, the Sustainable Grazing Systems (SGS) whole farm model to complement grazing experiments in assessing the effects of grazing strategies on beef cattle production. To determine rainfall variability impacts, twenty regression models were firstly developed between measured herbaceous AGB and, classical and extended multispectral vegetation indices (MVIs) derived from a Landsat 8 image. End-of-season herbaceous AGB was predicted with high accuracy (r2 range = 0.55 to 0.71; RMSE range = 840 to 1480 kgha-1). The most accurate model was used to construct a regression between rainfall and AGB derived from peak-season Landsat images available between 1992 and 2017. Standardised precipitation index and standardised anomalies of herbaceous AGB production were then used in a convergence of evidence approach to determine the response of AGB to rainfall variability and drought intensity. Total wet season rainfall revealed high variability (33 to 41 % CV) and subsequent herbaceous AGB production were 18 to 35 % more variable. Spatial heterogeneity of AGB production across herbaceous communities were high and deviated from mean AGB by 51 to 69 %. Landscape-level temporal variation of AGB production remained stable despite the increase of climate variability experienced in the region in the past 50 years. Climate inputs and parameter sets for upper-, mid- and foot- slope land types and key grass species, Urochloa mosambicensis and Eragrostis curvula were developed by integrating spatial data with previous soil surveys and extensive reviews of published experiments. A simulation experiment was conducted between 1992 and 2017 for all combinations of land types and grass species to analyse the extent of improvement resulting from parameter adjustments. The SGS model predicted the growth pattern known for grasses native to dry regions of southern Africa. The model represented measured herbaceous biomass moderately well (r2 = 0.57), at low average error (RMSE, 820 kg DM ha-1) despite huge discrepancies in summary statistics for measured (mean, 3877 kg DM ha-1) and simulated (mean, 3071 kg DM ha-1) biomass and residuals. Model predictions were also significantly correlated with remotely sensed AGB (r2 = 0.46) at reasonable overall performance error (RMSE, 981 kg DM ha-1). The integrated workflow developed for parameterising and calibrating the SGS pasture-simulation model can benefit model users in data-constrained environments. Animal growth parameters specific to Brahman weaner steers were defined in the SGS model to enable evaluation of impacts of recommended (10 haLU-1) and other three stocking rates (7, 15 and 20 haLU-1) and multi-paddock grazing systems (2-, 3- and 4- paddocks per herd) on rangeland productivity. Overall, there were no observable differences in herbage production and dry matter intake irrespective of stocking rate and multi-paddock grazing system. But stocking rate effects on animal production were more pronounced compared to multi-paddock grazing systems. To maximise cattle productivity in semi-arid rangelands, management should be emphasised on manipulation of stocking rates over multi-paddock grazing systems. Keywords Rangeland monitoring, climate risk, sustainability, animal productivity, grazing strategies / Thesis (PhD (Animal Production Management))--University of Pretoria, 2020. / National Research Foundation of South Africa / University of Pretoria Department of Research and Innovation Support / Animal and Wildlife Sciences / PhD / Unrestricted

During the last decades the challenge of sustainability has become more urgent and environmental impacts of different processes in manufacturing industry have received more attention. Life cycle assessment (LCA) has become an important and useful tool to evaluate the environmental impact of products and processes. In this study the environmental impact of two cooling techniques in a turning process has been evaluated using LCA. Turning is used for shaping metal parts by removing material. The compared cooling techniques in this study are dry cutting and Minimum Quantity Lubrication (MQL). The inputs and output in each technique are considered in form of material flows and energy consumption as well as waste flows. The Ecoinvent database has been used in order to quantify, evaluate and compare the environmental impacts of the two cooling techniques. Environmental impact categories considered in this study are Carbon footprint (CO2 kg equivalent), Cumulative Energy Demand (CED), Total eco-cost in Euro and ReCiPe. ReCiPe is a method used to evaluate multiple environmental impact categories and it covers impact categories related to human health, ecotoxicity and material depletion. Calculations and analysis of the results show that MQL has significantly lower environmental impact compared to dry cutting whereas energy consumption is the main contributor in the considered environmental impact categories. / Under de senaste åren har hållbar utveckling blivit mer relevant och miljöpåverkan av olika tillverkningsprocesser i industrin har således fått mer uppmärksamhet. Livscykelanalys (LCA) har blivit ett viktigt och användbart verktyg för att analysera och utvärdera miljöpåverkan av produkter och processer. I det här examensarbetet har miljöpåverkan av två olika kylmetoder vid svarvning utvärderats med hjälp av livscykelanalys (LCA). Svarvning används för att forma metalldelar. De jämförda kylmetoderna är torrskärning (dry cutting) utan kylvätska och minimalsmörjning (Minimum Quantity Lubrication - MQL) där en liten mängd smörjmedel används. Tillfört material, energiförbrukning och avfall vid varje kylmetod har betraktats. Ecoinvent-databasen har använts för att kvantifiera, utvärdera och jämföra miljöpåverkan av de två kylmetoderna. Miljöpåverkanskategorierna som behandlas i denna studie är koldioxidavtryck (CO2 kg ekvivalent), kumulativt energibehov (CED), totala miljökostnader i Euro och ReCiPe. ReCiPe är en metod som används för att utvärdera flera olika miljöpåverkanskategorier inkluderande människors hälsa, miljögifter och förbrukning av naturresurser. Beräkningarna och analysresultaten visar att MQL har betydligt lägre miljöpåverkan än torrskärning och att energiförbrukningen är den mest avgörande faktorn.