Numerical model estimation of biomethane production using an anaerobic CSTR: model formulation, parameter estimation and uncertainty/sensitivity analysis

Global climate change is becoming of increasing concern. Transportation makes up a large part of carbon gasses, which affects climate change and air quality. As transportation is a big part of carbon emissions, everybody can contribute to reducing emissions through transportation. One way for people to contribute is to start choosing greener transportation. Nuding is a tool that can be used to get people to choose greener transportation. It's function is to help guide people's behavior. For this project, the nudging goal is to nudge people towards healthier and greener transportation options than already in use. An example of a nudge is to provide reminders of bus departure times for a trip to an event. In order to nudge people gathering information relevant for traveling is necessary. In this thesis, relevant information for green transportation nudges is researched. Other studies on green transportation nudges are applied to discover relevant information topics and sources. Microservices architecture is proposed as the architecture for designing nudges, where the system is divided into smaller interconnected services that work together. Demonstrators of information collection microservices are designed and implemented. The demonstrators handle data for different information topics relevant to green transportation nudges. There are demonstrators for collecting weather data, routing data, public transportation data, rental bikes and scooters data, calendar data, and location data. The thesis also discusses how the data collected can be used to form transportation nudges

[i]In silico[/i] system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management

Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work

Sector-Based Water Demand Forecasting: Commercial Greenhouses

With rising electricity prices, forecasting water demand has become an essential part of the success of any water utility. Numerous forecasting methods have been suggested, but none have been able to characterize the unique consumer mixes that exist for every utility. This work focuses on a water utility located in Essex County, Ontario, Canada. Examination of the utilities consumer breakdown showed that almost 80% of their capacity was being consumed by commercial greenhouse operations. Current forecasting practices in this region for this sector are almost non-existent, assuming fixed demand for all greenhouse operations. This study presents three papers that focus on evaluation and simplification of forecasting techniques for commercial greenhouse operations. The first paper examines influential factors which drive greenhouse water consumption, with an emphasis on practicality. The second paper evaluates several forecasting model architectures ranging from elementary to complex in order to determine the most suitable method(s). The third paper compares water usage between two crops (tomatoes and bell peppers) in an effort to evaluate a crop to crop forecast technique that relies on one crops watering data in order to produce forecasts for another crop

Parameter sensitivity analysis for different complexity land surface models using multicriteria methods

A multicriteria algorithm, the MultiObjective Generalized Sensitivity Analysis (MOGSA), was used to investigate the parameter sensitivity of five different land surface models with increasing levels of complexity in the physical representation of the vegetation (BUCKET, CHASM, BATS 1, Noah, and BATS 2) at five different sites representing crop land/ pasture, grassland, rain forest, cropland, and semidesert areas. The methodology allows for the inclusion of parameter interaction and does not require assumptions of independence between parameters, while at the same time allowing for the ranking of several single-criterion and a global multicriteria sensitivity indices. The analysis required on the order of 50 thousand model runs. The results confirm that parameters with similar "physical meaning" across different model structures behave in different ways depending on the model and the locations. It is also shown that after a certain level an increase in model structure complexity does not necessarily lead to better parameter identifiability, i.e., higher sensitivity, and that a certain level of overparameterization is observed. For the case of the BATS 1 and BATS 2 models, with essentially the same model structure but a more sophisticated vegetation model, paradoxically, the effect on parameter sensitivity is mainly reflected in the sensitivity of the soil-related parameter. Copyright 2006 by the American Geophysical Union

Global sensitivity analysis in environmental water quality modelling: Where do we stand?

Global sensitivity analysis (GSA) is a valuable tool to support the use of mathematical models for environmental systems. During the last years the water quality modelling field has embraced the use of GSA. Environmental water quality modellers have tried to transfer the knowledge and experience acquired in other disciplines. The main objective of this paper is to provide an informed problem statement of the issues surrounding GSA applications in the environmental water quality modelling field. Specifically, this paper aims at identifying, for each GSA method, the potential use, the critical issues to be solved and the limits identified in a comprehensive literature review. The paper shows that the GSA methods are not mostly applied by using the numerical settings as suggested in the literature for other application fields. However, some authors have emphasized that the modeller must take care in employing such \u201cdefault\u201d numerical settings because,for complex water quality models, different GSA methods have been shown to provide different results depending on the settings. Quantitative convergence analysis has been identified as a key element for GSA quality control that merits further investigations for GSA application in the environmental water quality modelling field

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple "big-leaf" approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1×1°. While the photosynthetic capacity parameter (Vc;max25) is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity

Crop model parameter estimation and sensitivity analysis for large scale data using supercomputers

Doctor of PhilosophyDepartment of AgronomyStephen M. WelchGlobal crop production must be doubled by 2050 to feed 9 billion people. Novel crop improvement methods and management strategies are the sine qua non for achieving this goal. This requires reliable quantitative methods for predicting the behavior of crop cultivars in novel, time-varying environments. In the last century, two different mathematical prediction approaches emerged (1) quantitative genetics (QG) and (2) ecophysiological crop modeling (ECM). These methods are completely disjoint in terms of both their mathematics and their strengths and weaknesses. However, in the period from 1996 to 2006 a method for melding them emerged to support breeding programs. The method involves two steps: (1) exploiting ECM’s to describe the intricate, dynamic and environmentally responsive biological mechanisms determining crop growth and development on daily/hourly time scales; (2) using QG to link genetic markers to the values of ECM constants (called genotype-specific parameters, GSP’s) that encode the responses of different varieties to the environment. This can require huge amounts of computation because ECM’s have many GSP’s as well as site-specific properties (SSP’s, e.g. soil water holding capacity). Moreover, one cannot employ QG methods, unless the GSP’s from hundreds to thousands of lines are known. Thus, the overall objective of this study is to identify better ways to reduce the computational burden without minimizing ECM predictability. The study has three parts: (1) using the extended Fourier Amplitude Sensitivity Test (eFAST) to globally identify parameters of the CERES-Sorghum model that require accurate estimation under wet and dry environments; (2) developing a novel estimation method (Holographic Genetic Algorithm, HGA) applicable to both GSP and SSP estimation and testing it with the CROPGRO-Soybean model using 182 soybean lines planted in 352 site-years (7,426 yield observations); and (3) examining the behavior under estimation of the anthesis data prediction component of the CERES-Maize model. The latter study used 5,266 maize Nested Associated Mapping lines and a total 49,491 anthesis date observations from 11 plantings. Three major problems were discovered that challenge the ability to link QG and ECM’s: 1) model expressibility, 2) parameter equifinality, and 3) parameter instability. Poor expressibility is the structural inability of a model to accurately predict an observation. It can only be solved by model changes. Parameter equifinality occurs when multiple parameter values produce equivalent model predictions. This can be solved by using eFAST as a guide to reduce the numbers of interacting parameters and by collecting additional data types. When parameters are unstable, it is impossible to know what values to use in environments other than those used in calibration. All of the methods that will have to be applied to solve these problems will expand the amount of data used with ECM’s. This will require better optimization methods to estimate model parameters efficiently. The HGA developed in this study will be a good foundation to build on. Thus, future research should be directed towards solving these issues to enable ECM’s to be used as tools to support breeders, farmers, and researchers addressing global food security issues

Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions

This study conducted the global sensitivity analysis of the APSIM-Oryza rice growth model under eight climate conditions and two CO2 levels using the extended Fourier Amplitude Sensitivity Test method. Two output variables (i.e. total aboveground dry matter WAGT and dry weight of storage organs WSO) and twenty parameters were analyzed. The ±30% and ±50% perturbations of base values were used as the ranges of parameter variation, and local fertilization and irrigation managements were considered. Results showed that the influential parameters were the same under different environmental conditions, but their orders were often different. Climate conditions had obvious influence on the sensitivity index of several parameters (e.g. RGRLMX, WGRMX and SPGF). In particular, the sensitivity index of RGRLMX was larger under cold climate than under warm climate. Differences also exist for parameter sensitivity of early and late rice in the same site. The CO2 concentration did not have much influence on the results of sensitivity analysis. The range of parameter variation affected the stability of sensitivity analysis results, but the main conclusions were consistent between the results obtained from the ±30% perturbation and those obtained the ±50% perturbation in this study. Compared with existing studies, our study performed the sensitivity analysis of APSIM-Oryza under more environmental conditions, thereby providing more comprehensive insights into the model and its parameters