A pairwise likelihood approach for the empirical estimation of the underlyingvariograms in the plurigaussian models

The plurigaussian model is particularly suited to describe categorical regionalized variables. Starting from a simple principle, the thresh-olding of one or several Gaussian random fields (GRFs) to obtain categories, the plurigaussian model is well adapted for a wide range ofsituations. By acting on the form of the thresholding rule and/or the threshold values (which can vary along space) and the variograms ofthe underlying GRFs, one can generate many spatial configurations for the categorical variables. One difficulty is to choose variogrammodel for the underlying GRFs. Indeed, these latter are hidden by the truncation and we only observe the simple and cross-variogramsof the category indicators. In this paper, we propose a semiparametric method based on the pairwise likelihood to estimate the empiricalvariogram of the GRFs. It provides an exploratory tool in order to choose a suitable model for each GRF and later to estimate its param-eters. We illustrate the efficiency of the method with a Monte-Carlo simulation study .The method presented in this paper is implemented in the R packageRGeostats.Comment: To be submitted to Spatial Statistic

Recommendation domains for pond aquaculture

This publication introduces the methods and results of a research project that has developed a set of decision-support tools to identify places and sets of conditions for which a particular target aquaculture technology is considered feasible and therefore good to promote. The tools also identify the nature of constraints to aquaculture development and thereby shed light on appropriate interventions to realize the potential of the target areas. The project results will be useful for policy planners and decision makers in national, regional and local governments and development funding agencies, aquaculture extension workers in regional and local governments, and researchers in aquaculture systems and rural livelihoods. (Document contains 40 pages

Recommendation domains for pond aquaculture

This publication introduces the methods and results of a research project that has developed a set of decision-support tools to identify places and sets of conditions for which a particular target aquaculture technology is considered feasible and therefore good to promote. The tools also identify the nature of constraints to aquaculture development and thereby shed light on appropriate interventions to realize the potential of the target areas. The project results will be useful for policy planners and decision makers in national, regional and local governments and development funding agencies, aquaculture extension workers in regional and local governments, and researchers in aquaculture systems and rural livelihoods.Pond culture, Freshwater aquaculture, GIS

Enhanced watershed modeling and data analysis with a fully coupled hydrologic model and cloud-based flow analysis

2014 Summer.Includes bibliographical references.In today's world of increased water demand in the face of population growth and climate change, there are no simple answers. For this reason many municipalities, water resource engineers, and federal analyses turn to modeling watersheds for a better understanding of the possible outcomes of their water management actions. The physical processes that govern movement and transport of water and constituents are typically highly nonlinear. Therefore, improper characterization of a complex, integrated, processes like surface-subsurface water interaction can substantially impact water management decisions that are made based on existing models. Historically there have been numerous tools and watershed models developed to analyze watersheds or their constituent components of rainfall, run-off, irrigation, nutrients, and stream flow. However, due to the complexity of real watershed systems, many models have specialized at analyzing only a portion of watershed processes like surface flow, subsurface flow, or simply analyzing local monitoring data rather than modeling the system. As a result many models are unable to accurately represent complex systems in which surface and subsurface processes are both important. Two popular watershed models have been used extensively to represent surface processes, SWAT (Arnold et al, 1998), and subsurface processes, MODFLOW (Harbaugh, 2005). The lack of comprehensive watershed simulation has led to a rise in uncertainty for managing water resources in complex surface-subsurface driven watersheds. For this reason, there have been multiple attempts to couple the SWAT and MODFLOW models for a more comprehensive watershed simulation (Perkins and Sophocleous, 1999; Menking, 2003; Galbiati et al., 2006; Kim et al., 2008); however, the previous couplings are typically monthly couplings with spatial restrictions for the two models. Additionally, most of these coupled SWAT-MODFLOW models are unavailable to the general public, unlike the constituent SWAT and MODFLOW models which are available. Furthermore, many of these couplings depend on a forced equal spatial discretization for computational units. This requires that one MODFLOW grid cell is the same size and location of one SWAT hydrologic response unit (HRU). Additionally, many of the previous couplings are based on a loose monthly average coupling which might be insufficient in natural spring and irrigated agricultural driven groundwater systems which can fluctuate on a sub-monthly time scale. The primary goal of this work is to enhance the capacity for modeling watershed processes by fully coupling surface and subsurface hydrologic processes at a daily time step. The specific objectives of this work are 1) to examine and create a general spatial linkage between SWAT and MODFLOW allowing the use of spatially-different existing models for coupling; 2) to examine existing practices and address current weaknesses for coupling of the SWAT and MODFLOW models to develop an integrated modeling system; 3) to demonstrate the capacity of the enhanced model compared to the original SWAT and MODFLOW models on the North Fork of the Sprague River in the Upper Klamath Basin in Oregon. The resulting generalized daily coupling between a spatially dis-similar SWAT and MODFLOW model on the North Fork of the Sprague River has resulted in a slightly more lower representation of monthly stream flow (monthly R2 = 0.66, NS = 0.38) than the original SWAT model (monthly R2 = 0.60, NS = 0.57) with no additional calibration. The Log10 results of stream flow illustrate an even greater improvement between SWAT-MODFLOW correlation (R2) but not the overall simulation (NS) (monthly R2 = 0.74, NS = -0.29) compared to the original SWAT (monthly R2 = 0.63, NS = 0.63) correlation (R2). With an improved water table representation, these SWAT-MODFLOW simulation results illustrate a more in depth representation of overall stream flows on a groundwater influenced tributary of the Sprague River than the original SWAT model. Additionally, with the increased complexity of environmental models there is a need to design and implement tools that are more accessible and computationally scalable; otherwise their use will remain limited to those that developed them. In light of advancements in cloud-computing technology a better implementation of modern desktop software packages would be the use of scalable cloud-based cyberinfrastructure, or cloud-based environmental modeling services. Cloud-based deployment of water data and modeling tools assist in a scalable as well as platform independent analysis; meaning a desktop, laptop, tablet, or smart phone can perform the same analyses. To utilize recent advancements in computer technology, a further focus of this work is to develop and demonstrate a scalable cloud-computing web-tool that facilitates access and analysis of stream flow data. The specific objectives are to 1) unify the various stream flow analysis topics into a single tool; 2) to assist in the access to data and inputs for current flow analysis methods; 3) to examine the scalability benefits of a cloud-based flow analysis tool. Furthermore, the new Comprehensive Flow Analysis tool successfully combined time-series statistics, flood analysis, base-flow separation, drought analysis, duration curve analysis, and load estimation into a single web-based tool. Preliminary and secondary scalability testing has revealed that the CFA analyses are scalable in a cloud-based cyberinfrastructure environment to a request rate that is likely unrealistic for web tools

Restricted Covariance Priors with Applications in Spatial Statistics

We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance--covariance matrix. Specifically, we introduce a new distribution called the truncated G-Wishart distribution that has support over precision matrices that lead to positive associations between the random effects of neighboring regions while preserving conditional independence of non-neighboring regions. We describe Markov chain Monte Carlo sampling algorithms for the truncated G-Wishart prior in a disease mapping context and compare our results to Bayesian hierarchical models based on intrinsic autoregression priors. A simulation study illustrates that using the truncated G-Wishart prior improves over the intrinsic autoregressive priors when there are discontinuities in the disease risk surface. The new model is applied to an analysis of cancer incidence data in Washington State.Comment: Published at http://dx.doi.org/10.1214/14-BA927 in the Bayesian Analysis (http://projecteuclid.org/euclid.ba) by the International Society of Bayesian Analysis (http://bayesian.org/

Should We Account for Network Distances or Anisotropy in the Spatial Estimation of Missing Traffic Data?

In light of the unavailability of traffic volume data for all road segments, the scientific literature proposes estimating this variable using spatial interpolators. However, most of the methods found use the Euclidean distance between the database points as a proximity measure, in addition to ignoring the anisotropy of the phenomenon. Thus, the objective of the present study was to apply Ordinary Kriging (OK) with network distances and anisotropic OK in traffic volume data on highways in the state of São Paulo (Brazil), comparing its results to the traditional isotropic approach with Euclidean distances. Goodness-of-fit measures confirmed the good performance and better suitability of OK with network distances over the analyses that use Euclidean distances. Addressing the anisotropy of the traffic volume data also helped to improve the results. The proposed method can effectively support estimating traffic volume in segments without flow data

The transitional-probability Markov chain versus traditional indicator methods for modeling the geotechnical categories in a test site.

Das Ziel der vorliegenden Arbeit war die Erstellung eines dreidimensionalen Untergrundmodells der Region Göttingen basierend auf einer geotechnischen Klassifikation der unkosolidierten Sedimente. Die untersuchten Materialen reichen von Lockersedimenten bis hin zu Festgesteinen, werden jedoch in der vorliegenden Arbeit als Boden, Bodenklassen bzw. Bodenkategorien bezeichnet. Diese Studie evaluiert verschiedene Möglichkeiten durch geostatistische Methoden und Simulationen heterogene Untergründe zu erfassen. Derartige Modellierungen stellen ein fundamentales Hilfswerkzeug u.a. in der Geotechnik, im Bergbau, der Ölprospektion sowie in der Hydrogeologie dar. Eine detaillierte Modellierung der benötigten kontinuierlichen Parameter wie z. B. der Porosität, der Permeabilität oder hydraulischen Leitfähigkeit des Untergrundes setzt eine exakte Bestimmung der Grenzen von Fazies- und Bodenkategorien voraus. Der Fokus dieser Arbeit liegt auf der dreidimensionalen Modellierung von Lockergesteinen und deren Klassifikation basierend auf entsprechend geostatistisch ermittelten Kennwerten. Als Methoden wurden konventionelle, pixelbasierende sowie übergangswahrscheinlichkeitsbasierende Markov-Ketten Modelle verwendet. Nach einer generellen statistischen Auswertung der Parameter wird das Vorhandensein bzw. Fehlen einer Bodenkategorie entlang der Bohrlöcher durch Indikatorparameter beschrieben. Der Indikator einer Kategorie eines Probepunkts ist eins wenn die Kategorie vorhanden ist bzw. null wenn sie nicht vorhanden ist. Zwischenstadien können ebenfalls definiert werden. Beispielsweise wird ein Wert von 0.5 definiert falls zwei Kategorien vorhanden sind, der genauen Anteil jedoch nicht näher bekannt ist. Um die stationären Eigenschaften der Indikatorvariablen zu verbessern, werden die initialen Koordinaten in ein neues System, proportional zur Ober- bzw. Unterseite der entsprechenden Modellschicht, transformiert. Im neuen Koordinatenraum werden die entsprechenden Indikatorvariogramme für jede Kategorie für verschiedene Raumrichtungen berechnet. Semi-Variogramme werden in dieser Arbeit, zur besseren Übersicht, ebenfalls als Variogramme bezeichnet. IV Durch ein Indikatorkriging wird die Wahrscheinlichkeit jeder Kategorie an einem Modellknoten berechnet. Basierend auf den berechneten Wahrscheinlichkeiten für die Existenz einer Modellkategorie im vorherigen Schritt wird die wahrscheinlichste Kategorie dem Knoten zugeordnet. Die verwendeten Indikator-Variogramm Modelle und Indikatorkriging Parameter wurden validiert und optimiert. Die Reduktion der Modellknoten und die Auswirkung auf die Präzision des Modells wurden ebenfalls untersucht. Um kleinskalige Variationen der Kategorien auflösen zu können, wurden die entwickelten Methoden angewendet und verglichen. Als Simulationsmethoden wurden "Sequential Indicator Simulation" (SISIM) und der "Transition Probability Markov Chain" (TP/MC) verwendet. Die durchgeführten Studien zeigen, dass die TP/MC Methode generell gute Ergebnisse liefert, insbesondere im Vergleich zur SISIM Methode. Vergleichend werden alternative Methoden für ähnlichen Fragestellungen evaluiert und deren Ineffizienz aufgezeigt. Eine Verbesserung der TP/MC Methoden wird ebenfalls beschrieben und mit Ergebnissen belegt, sowie weitere Vorschläge zur Modifikation der Methoden gegeben. Basierend auf den Ergebnissen wird zur Anwendung der Methode für ähnliche Fragestellungen geraten. Hierfür werden Simulationsauswahl, Tests und Bewertungsysteme vorgeschlagen sowie weitere Studienschwerpunkte beleuchtet. Eine computergestützte Nutzung des Verfahrens, die alle Simulationsschritte umfasst, könnte zukünftig entwickelt werden um die Effizienz zu erhöhen. Die Ergebnisse dieser Studie und nachfolgende Untersuchungen könnten für eine Vielzahl von Fragestellungen im Bergbau, der Erdölindustrie, Geotechnik und Hydrogeologie von Bedeutung sein.Having a plenty of geotechnical records and measurements in Göttingen area, a subsurface three-dimensional model of the unconsolidated sediment classes was required. To avoid the repetition of the long expressions, from this point on, these unconsolidated materials which vary from the loose sediments to the hard rocks has been termed as “soil”, “category”, “soil class” or “soil category”. These sediments which are intermediate between the hard bed-rock and loose sediments (soils) were categorized based on the geotechnical norms of the DIN 18196. In this study, the aim was to evaluate the capabilities of the application of geostatistical estimation and simulation methods in modeling the subsurface heterogeneities, especially about the geotechnical soil classes. Such a heterogeneity modeling is a crucial step in a variety of applications such as geotechnics, mining, petroleum engineering, hydrogeology, and so on. For an accurate modeling of the essential continuous parameters, such as the ore grades, porosity, permeability, and hydraulic conductivity of a porous medium, the precise delineation of the facies or soil category boundaries prior to any modeling step is necessary. The focus of this study is on a three-dimensional modeling and delineation of the unconsolidated materials of the subsurface using the geostatistical methods. The applied geostatistical methods here consisted of the pixel-based conventional and transition-probability Markov chain-based geostatistical methods. After a general statistical evaluation of different parameters, the presence and absence of each category along the sampling boreholes was coded by new parameters called indicators. The indicator of a category in a sampling point is one (1) when the category exists and zero (0) when it is absent. Some intermediate states can also be found. For instance, the indicator of a two categories can be assigned to 0.5 when both the categories probably exist at that location but it is unsure which one exactly presents at that location. Moreover, to increase the stationarity characteristic of the indicator variables, the initial coordinates were transformed into a new system proportional to the top and bottom of the modeled layer as a first modeling step. In the new space, to conduct the conventional geostatistical modeling, the indicator variograms were calculated and modeled for each category in a variety of directions. In this text, for easier reference to the semi-variograms, the term variogram has been applied instead. II Using the indicator kriging, the probability of the occurrence of each category at each modeling node was estimated. Based on the estimated probabilities of the existence of each soil category from the previous stage, the most probable category was assigned to each modeling point then. Moreover, the employed indicator variogram models and indicator kriging estimation parameters were validated and improved. The application of a less number of samples were also tested and suggested for similar cases with a comparable precision in the results. To better reflect the fine variations of the categories, the geostatistical simulation methods were applied, evaluated, and compared together. The employed simulation methods consisted of the sequential indicator simulation (SISIM) and the transition probability Markov chain (TP/MC). The conducted study here suggested that the TP/MC method could generate satisfactory results especially compared to those of the SISIM method. Some reasons were also brought and discussed for the inefficiency of the other facies modeling alternatives for this application (and similar cases). Some attempts for improving the TP/MC method were also conducted and a number of results and suggestions for further researches were summarized here. Based on the achieved results, the application of the TP/MC methods was advised for the similar problems. Besides, some simulation selection, tests, and assessment frameworks were proposed for analogous applications. In addition, some instructions for future studies were made. The proposed framework and possibly the improved version of it could be further completed by creating a guided computer code that would contain all of the proposed steps. The results of this study and probably its follow-up surveys could be of an essential importance in a variety of important applications such as geotechnics, hydrogeology, mining, and hydrocarbon reservoirs

A System Dynamics Based Integrated Assessment Modelling of Global-Regional Climate Change: A Model for Analyzing the Behaviour of the Social-Energy-Economy-Climate System

The feedback based integrated assessment model ANEMI (version 2) represents the society-biosphere-climate-economy-energy system of the earth and biosphere. The development of the ANEMI model version 2 is based on the system dynamics simulation approach that (a) allows for the understanding and modelling of complex global change and (b) assists in the investigation of possible policy options for mitigating, and/or adopting to changing global conditions within an integrated assessment modelling framework. This thesis presents the ANEMI model version 2 and its nine individual sectors: climate, carbon cycle, land-use, population, food production, hydrologic cycle, water demand, water quality, and energy-economy. Two levels of the model are developed and presented here. The first one represents the society-biosphere-climate-economy-energy system on a global scale (ANEMI version 2). The second one is developed for a regional presentation of Canada (ANEMI_CDN). The development of the Canada model is based on the top-down approach and various disaggregation techniques. The disaggregation technique also extends the capability of the ANEMI model version 2 in generating monthly data, while the model runs with yearly time step. To evaluate market and nonmarket costs and benefits of climate change, the ANEMI model integrates an economic approach, with a focus on the international energy stock and fuel price, with climate interrelations and temperature change. The model takes into account all major greenhouse gases (GHG) influencing global temperature and sea-level variation. Several of the model sectors are built from the basic structure of the previous version of the ANEMI model (version 1.2) developed by Davies (2009) and reported by Davies and Simonovic (2010; 2011). However, they are integrated in a novel way, particularly the water sectors. The integration of optimization within the simulation framework of the ANEMI model version 2 is timely, as recognition grows of the importance of energy-based economic activities in determining long-term Earth-system behaviour. Experimentation with different policy scenarios demonstrates the consequences of these activities on future behaviour of the society-biosphere-climate-economy-energy system through feedback based interactions. The use of the model ANEMI version 2 and ANEMI_CDN improves both scientific understanding and socio-economic policy development strategy. This thesis describes the model structure in detail and illustrates its use through the analysis of three policy scenarios in both global and Canadian perspectives

Spatial Estimation of Depth to Bedrock using Borehole Data: A Gaussian Process Framework

Depth to Bedrock (DTB) is a critical parameter in several fields of study, including geology, hydrology, soil sciences, and civil engineering. However, obtaining this parameter through near-surface geophysical methods can be challenging and expensive, particularly in difficult terrain. Fortunately, high-quality borehole data from previous geotechnical investigations can be used to estimate the DTB in areas where no boreholes have yet been created. This thesis presents a machine learning framework for estimating the DTB value in areas of interest using Gaussian Process models. The performance of different kernel functions, including Radial Basis Function (RBF), Matérn 3/2 kernels, and combined linear and RBF kernels, is evaluated, along with the impact of implementing anisotropy in the models. The results show that the Matérn 3/2 kernel with anisotropic implementation performs the best in estimating DTB. However, challenges in hyperparameter optimization, non-Gaussian target variables, and model selection are highlighted, and further investigation into these areas is recommended. The framework presented here provides practical implications for geotechnical engineering. Further, it provides a basis for future research in this area, where the incorporation of additional geological and remotely sensed data could potentially improve the quality of DTB estimation