Developing Efficient Strategies For Global Sensitivity Analysis Of Complex Environmental Systems Models

Complex Environmental Systems Models (CESMs) have been developed and applied as vital tools to tackle the ecological, water, food, and energy crises that humanity faces, and have been used widely to support decision-making about management of the quality and quantity of Earth’s resources. CESMs are often controlled by many interacting and uncertain parameters, and typically integrate data from multiple sources at different spatio-temporal scales, which make them highly complex. Global Sensitivity Analysis (GSA) techniques have proven to be promising for deepening our understanding of the model complexity and interactions between various parameters and providing helpful recommendations for further model development and data acquisition. Aside from the complexity issue, the computationally expensive nature of the CESMs precludes effective application of the existing GSA techniques in quantifying the global influence of each parameter on variability of the CESMs’ outputs. This is because a comprehensive sensitivity analysis often requires performing a very large number of model runs. Therefore, there is a need to break down this barrier by the development of more efficient strategies for sensitivity analysis. The research undertaken in this dissertation is mainly focused on alleviating the computational burden associated with GSA of the computationally expensive CESMs through developing efficiency-increasing strategies for robust sensitivity analysis. This is accomplished by: (1) proposing an efficient sequential sampling strategy for robust sampling-based analysis of CESMs; (2) developing an automated parameter grouping strategy of high-dimensional CESMs, (3) introducing a new robustness measure for convergence assessment of the GSA methods; and (4) investigating time-saving strategies for handling simulation failures/crashes during the sensitivity analysis of computationally expensive CESMs. This dissertation provides a set of innovative numerical techniques that can be used in conjunction with any GSA algorithm and be integrated in model building and systems analysis procedures in any field where models are used. A range of analytical test functions and environmental models with varying complexity and dimensionality are utilized across this research to test the performance of the proposed methods. These methods, which are embedded in the VARS–TOOL software package, can also provide information useful for diagnostic testing, parameter identifiability analysis, model simplification, model calibration, and experimental design. They can be further applied to address a range of decision making-related problems such as characterizing the main causes of risk in the context of probabilistic risk assessment and exploring the CESMs’ sensitivity to a wide range of plausible future changes (e.g., hydrometeorological conditions) in the context of scenario analysis

How certain are we about the model-based estimations of global irrigation water withdrawal?

Irrigation agriculture is the most important user of the global freshwater resources worldwide, which makes it one of the key actors conditioning sustainable development and water security. The anticipated future climate change, population growth, and rapidly rising global demand for food will likely lead to agricultural expansion by allowing the development of irrigated areas. This together with the fact that irrigated crops are approximately four times more profitable than rainfed crops will place much additional pressure on water resources in the next years. Therefore, it is of vital importance to devise solutions that minimize the negative impacts of agricultural expansion, particularly on biodiversity and water use, so as to help us achieve environmental and economic sustainability. To realize such an ambition, quantifying irrigation water withdrawal at different spatio-temporal scales is essential. Global Hydrological Models (GHM) are often used to produce irrigation water withdrawal estimates. Yet GHMs questionably rely on several uncertain estimates of irrigated areas, crop evapotranspiration processes, precipitation and irrigation efficiency, which are the four main inputs in the structure of GHMs. Here we show that, once basic uncertainties regarding these estimates are properly integrated into the calculations, the point-based irrigation water withdrawal estimates actually correspond to uncertainty intervals that span several orders of magnitude already at the grid cell level. Our approach is based on the concept of sensitivity auditing, a practice of process-oriented skepticism towards mathematical models. The numerical results suggest that current estimates of global irrigation water withdrawals are spuriously accurate due to their neglect of several ambiguities/uncertainties, and thus need to be re-assessed. Our analysis highlights that models of global irrigation water demands need to better integrate uncertainties, both technical and epistemological, so as to avoid misguiding the development of strategies intended to help ensure water and food security

The future of sensitivity analysis: an essential discipline for systems modeling and policy support

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society

Unpacking the modelling process via sensitivity auditing

Acknowledging the conditionality of model-based evidence facilitates the dialogue between model developers and model users, especially when models are used to guide decisions at the science-policy interface. In general, model users have limited access to verify the realism of a model, being only exposed to model plausibility and trustworthiness; instead, modellers have an an array of validation and verification techniques available. In the end, model credibility is what both developers and users aim for, also in the interest of shielding from the possible pitfall of over-interpreting the model results. To this end, in this contribution we discuss sensitivity auditing, an extension of sensitivity analysis, that can help model developers and users to overcome communication barriers and foster dialogue around modelling activities. The use of sensitivity auditing is not limited to models in a restricted sense, but it can be applied to any policy-relevant instance of quantification, including metrics, rankings and indicators. We present six real-world applications of sensitivity auditing to instances of quantification in a range of socio-environmental systems, including public health, education, and the water-food nexus. These examples reveal the usefulness of sensitivity auditing in facilitating the proper use of numbers and models at the science-policy- society interface and avoiding uncertainty laundering

Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational cost

Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to highdimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs.JRC.I.1-Monitoring, Indicators & Impact Evaluatio

Polymerase Chain Reaction Assay Using the Restriction Fragment Length Polymorphism Technique in the Detection of Prosthetic Joint Infections: A Multi-Centered Study

Background: PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) techniques have been used for the diagnosis of bacteria in some infections. In this study, we aimed to evaluate the diagnostic accuracy of PCR for the diagnosis of prosthetic joint infections (PJI) and to identify isolated microorganisms, using the RFLP method. Methods: During January 2015 to January 2018, patients who were suspected of having PJI after arthroplasty surgery or were candidates for revision surgery due to loosening of implant entered the study. Patients who had 1 major criterion or 3 minor criteria for PJI based on the Philadelphia Consensus Criteria (PCC) on Periprosthetic Joint Infection were considered as cases of PJI. Both culture results and PCR findings, were cross compared with results of the PCC (as the gold standard criteria). Results: Overall, 76 samples were included in the study. Mean (standard deviation) age of patients was 66.72 ± 11.82 years. Overall, 57.9 of patients were females. Prevalence of PJI was 50 based on the PCC. Sensitivity, specificity, positive predictive value, negative predictive value, and general efficacy of PCR for detection of PJI was 97.4, 100, 100, 97.4, and 98.7, respectively. Sensitivity, specificity, positive predictive value, negative predictive value, and general efficacy of culture was 31.6, 100, 65.7, 100, and 59.4, respectively. We isolated a broad range of bacteria using PCR-RFLP including Gram-positive cocci such as Staphylococcus sp., Streptococcus sp., and Enterococcus sp., and Gram-negative bacilli such as Enterobacteriaceae sp., Pseudomonas sp. Citrobacter sp., as well as Chlamydophila pneumonia, Stenotrophomonas maltophilia, Brucella melitensis, non-gonococcal Neisseria, Kingella kingae, Bacteroides ovatus, and Proteus mirabilis from PJI patients. Conclusion: Inhere, for the first time, we showed that PCR-RFLP is a powerful tool for identifying the type of bacteria involved in PJI, and can be used for follow-up of patients suspected of PJI and those with a history of antibiotic use. PCR-RFLP may be able to substantially decrease detection time of PJI among PCR-based methods, while allowing more accurate identification of the bacteria involved. © 2018 Elsevier Inc

The delusive accuracy of global irrigation water withdrawal estimates

Miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management. Hydrological models quantify water withdrawals, but their estimates are unduly precise. Model imperfections need to be appreciated to avoid policy misjudgements