Dissertations / Theses on the topic 'Water column data analysis'

This study investigated the efficiency of the available open-source columnbased storage formats with support for semi-flexible data in combination with query engines that support querying these formats. Two different formats were identified, Parquet and ORC, and both were tested in two different modes, uncompressed and compressed with the compression algorithm Snappy. They were tested by running two queries on the host company’s data converted to the appropriate formats, one simple averaging query and one more complicated with counts and filtering. The queries were run with two different query engines, Spark and Drill. They were also run on two dataset with different sizes to test scalability. The query execution time was recorded for each tested alternative. The results show that Snappy compressed formats always outperformed their non-compressed counterparts, and that Parquet was always faster than ORC. Drill performed faster on the simple query while Spark performed faster on the complex query. Drill also had the least increase in query execution time when the size of the dataset increased on both queries. The conclusion is that Parquet with Snappy is the storage format which gives the fastest execution times. However, both Spark and Drill have their own advantages as query engines.
Denna studie undersökte effektiviteten av de i öppen källkod tillgängliga kolumnbaserade lagringsformaten med stöd för semistrukturerad data i kombination med frågemotorer som stödjer dessa format. Två olika format identifierades, Parquet och ORC, och båda testades i på olika sätt, okomprimerade och komprimerade med kompressionsalgoritmen Snappy. De testades genom att köra två frågor på uppdragsgivarens data som konverterades till de testade formaten, en enkel som räknar genomsnitt och en mer komplicerad med radräkning och filtrering. Båda frågorna kördes med två olika frågemotorer, Spark and Drill. De kördes på två datamängder med olika storlekar för att testa skalbarhet. Exekveringstiden mättes för varje testat alternativ. Resultaten visar att Snappy-komprimerade format alltid exekverade snabbare än de ickekomprimerade formaten, och att Parquet alltid körde snabbare än ORC. Drill var snabbare på den enkla frågan medan Spark var snabbare på den komplexa. Drill hade också den minsta ökningen i exekveringstiden när storleken på datamängden ökade på båda frågorna. Slutsatsen är att Parquet med Snappy är det lagringsformat som ger den snabbaste exekveringstiden, och att både Spark och Drill har sina egna fördelar.

Although oscillating water column type wave energy devices are nearing the stage of commercial exploitation, there is still much to be learnt about many facets of their hydrodynamic performance. This research uses the commercially available FLUENT computational fluid dynamics flow solver to model a complete OWC system in a two dimensional numerical wave tank. A key feature of the numerical modelling is the focus on the influence of the front wall geometry and in particular the effect of the front wall aperture shape on the hydrodynamic conversion efficiency. In order to validate the numerical modelling, a 1:12.5 scale experimental model has been tested in a wave tank under regular wave conditions. The effects of the front lip shape on the hydrodynamic efficiency are investigated both numerically and experimentally and the results compared. The results obtained show that with careful consideration of key modelling parameters as well as ensuring sufficient data resolution, there is good agreement between the two methods. The results of the testing have also illustrated that simple changes to the front wall aperture shape can provide marked improvements in the efficiency of energy capture for OWC type devices.

Marine aggregates are an important means of carbon transfers downwards to the deep ocean as well as an important nutritional source for benthic organism communities that are the ultimate recipients of the flux. During these last 10 years, data on size distribution of particulate matter have been collected in different oceanic provinces using an Underwater Video Profiler. The cruise data include simultaneous analyses of particle size distributions as well as additional physical and biological measurements of water properties through the water column. First, size distributions of large aggregates have been compared to simultaneous measurements of particle flux observed in sediment traps. We related sediment trap compositional data to particle size (d) distributions to estimate their vertical fluxes (F) using simple power relationships (F=Ad^b). The spatial resolution of sedimentation processes allowed by the use of in situ particle sizing instruments lead to a more detailed study of the role of physical processes in vertical flux. Second, evolution of the aggregate size distributions with depth was related to overlying primary production and phytoplankton size-distributions on a global scale. A new clustering technique was developed to partition the profiles of aggregate size distributions. Six clusters were isolated. Profiles with a high proportion of large aggregates were found in high-productivity waters while profiles with a high proportion of small aggregates were located in low-productivity waters. The aggregate size and mass flux in the mesopelagic layer were correlated to the nature of primary producers (micro-, nano-, picophytoplankton fractions) and to the amount of integrated chlorophyll a in the euphotic layer using a multiple regression technique on principal components. Finally, a mesoscale area in the North Atlantic Ocean was studied to emphasize the importance of the physical structure of the water column on the horizontal and vertical distribution of particulate matter. The seasonal change in the abundance of aggregates in the upper 1000 m was consistent with changes in the composition and intensity of the particulate flux recorded in sediment traps. In an area dominated by eddies, surface accumulation of aggregates and export down to 1000 m occured at mesoscale distances (<100 km).

Physical-chemical water quality data from lotic water monitoring sites across Canada were integrated into one dataset. Two overlapping matrices of data were analyzed with principal component analysis (PCA) and cluster analysis to uncover structure and patterns in the data. The first matrix (Matrix A) had 107 sites located throughout Canada, and the following water quality parameters: pH, specific conductance (SC), and total phosphorus (TP). The second matrix (Matrix B) included more variables: calcium (Ca), chloride (Cl), total alkalinity (T_ALK), dissolved oxygen (DO), water temperature (WT), pH, SC and TP; for a subset of 42 sites. Landscape characteristics were calculated for each water quality monitoring site and their importance in explaining water quality data was examined through redundancy analysis. The first principal components in the analyses of Matrix A and B were most correlated with SC, suggesting this parameter is the most representative of water quality variance at the scale of Canada. Overlaying cluster analysis results on PCA information proved an excellent mean to identify the major water characteristics defining each group; mapping cluster analysis group membership provided information on their spatial distribution and was found informative with regards to the probable environmental influences on each group. Redundancy analyses produced significant predictive models of water quality demonstrating that landscape characteristics are determinant factors in water quality at the country scale. The proportion of cropland and the mean annual total precipitation in the drainage area were the landscape variables with the most variance explained. Assembling a consistent dataset of water quality data from monitoring locations throughout Canada proved difficult due to the unevenness of the monitoring programs in place. It is therefore recommended that a standard for the monitoring of a minimum core set of water quality variable be implemented throughout the country to support future nation-wide analysis of water quality data.

Providing safe and high quality drinking water is essential for a high quality of life. However, the water resources in Europe are threatened by various sources of contamination. This has led to the development of concepts and technologies to create a basis for provision of safe and high quality drinking water, which had thus resulted in the formation of the Artificial Recharge Demonstration project (ARTDEMO). The overall aim of this thesis in relation to the ARTDEMO project was to develop a realtime automated water monitoring system, capable of using data from various complementary sources to determine the amounts of inorganic and organic pollutants. The application of multivariate calibration to differential pulse anodic stripping voltammograms and fluorescence spectra (emission and excitation-emission matrix) is presented. The quantitative determination of cadmium, lead and copper acquired on carbon-ink screen-printed electrodes, arsenic and mercury acquired on gold-ink screen-printed electrodes, in addition to the quantitative determination of anthracene, phenanthrene and naphthalene have been realised. The statistically inspired modification of partial least squares (SIMPLS) algorithm has been shown to be the better modelling tool, in terms of the root mean square error of prediction (RMSEP), in conjunction with application of data pre-treatment techniques involving rangescaling, filtering and weighting of variables. The % recoveries of cadmium, lead and copper in a certified reference material by graphite furnace atomic absorption spectrometry (GF-AAS) and multivariate calibration are in good agreement. The development of a prototype application on a personal digital assistant (PDA) device is described. At-line analysis at potential contamination sites in which an instant response is required is thus possible. This provides quantitative screening of target metal ions. The application imports the acquired voltammograms, standardises them against the laboratory-acquired voltammograms (using piecewise direct standardisation), and predicts the concentrations of the target metal ions using previously trained SIMPLS models. This work represents significant progress in the development of analytical techniques for water quality determination, in line with the ARTDEMO project's aim of maintaining a high quality of drinking water.

Analyses of EPA long-term datasets (1985--1994) combined with field studies and ecosystem model development were used to investigate phytoplankton and nutrient dynamics in the York River estuary. Analysis of the EPA dataset showed that algal blooms occurred during winter-spring followed by smaller summer blooms. Peak phytoplankton biomass during the winter-spring blooms occurred in the mid reach of the mesohaline zone whereas during the summer bloom it occurred in the tidal fresh-mesolialine transition zone. River discharge appears to be the major factor controlling the location and timing of the winter-spring blooms and the relative degree of potential nitrogen (N) and phosphorus (P) limitation. Phytoplankton biomass in tidal fresh water regions was limited by high flushing rates. Water residence time was less than cell doubling rate during seasons of high river flow. Positive correlations between PAR at 1m depth and chlorophyll a suggested light limitation of phytoplankton in the tidal fresh-mesohaline transition zone. A significant relationship between the delta of salinity between surface and bottom water and chlorophyll a distribution suggested the importance of tidal mixing for phytoplankton dynamics in the mesohaline zone. Accumulation of phytoplankton biomass in the mesohaline zone was generally controlled by N with the nutrient supply provided by benthic or bottom water remineralization. In general, phytoplankton dynamics appear controlled to a large extent by resource limitation (bottom-up control) rather than zooplankton grazing (top-down control). The dynamics of phytoplankton size structure were investigated in the freshwater, transitional and estuarine reaches of the York River over an annual cycle. The contribution of large cells (micro-plankton, >20 mum) to total biomass increased downstream during winter whereas that of small cells (nano-, 3--20 mum) pico-plankton, <3 mum) increased downstream during summer. I conclude from these studies that spatial and seasonal variations in size structure of phytoplankton observed on the estuarine scale are determined both by the different preferences of micro-, nano-, and picoplankton for nutrients and by their different light requirements. Analyses of phytoplankton size structure are, thus, necessary to better understand phytoplankton dynamics and to better manage water quality in estuarine systems. An ecosystem model was developed to integrate these data and to investigate mechanisms controlling the size-structured phytoplankton dynamics in the mesohaline zone of the York River estuary. The model developed in Fortran90 included 12 state variables describing the distribution of carbon and nutrients (nitrogen, phosphorus) in the surface mixed layer. Forcing functions included incident radiation, temperature, wind stress, mean flow and tide including advective transport and turbulent mixing. Model results supported the general view that phytoplankton dynamics are controlled by abiotic mechanisms (i.e. bottom-up control) rather than biotic, trophic interactions in the York River estuary. Model sensitivity tests showed that small cells (pico-, nano-sized) are more likely regulated by temperature and light whereas large cells (micro-sized) are regulated by physical processes such as advection, and tidal mixing. Microphytoplankton blooms during winter- pring resulted from a combination of longitudinal advection and vertical diffusion of phytoplankton cells rather than in-situ production.

The growing field of IoT increases the amount of time series data produced every day. With such information overload it is necessary to promptly clean and process those information extracting meaningful knowledge and avoiding raw data storage. Nowadays cloud infrastructures allow to adopt this processing demand by providing new models for defining data-parallel processing pipelines, such as the Apache Beam unified model which evolved from Google Cloud Dataflow and MapReduce paradigm. The projects of this thesis have been implemented during a three-month internship at Injenia srl, and face this exact trail, by processing external IoT-acquired data, going through a cleansing and a processing phase in order to obtain neural networks ready-to-feed data. The sewerage project acquires signals from IoT sensors of a sewerage infrastructure and aims at predicting signals' trends over close future periods. The aqueduct project acquires the same information type from aqueduct plants and aims to reduce the false alarm rate of the telecontrol system. Given the good results of both projects it can be concluded that the data processing phase has produced high-quality information which is the main objective of this thesis.

The analysis of organic pollutants in water is traditionally done following EPA procedures which commonly use liquid-liquid extraction. One liter of water is extracted three times with 60 mL of an organic solvent. The extract is concentrated and analyzed by gas chromatography. This procedure is time consuming and can cause losses of semi-volatile components, in addition to requiring a relatively large amount of organic solvent (180 mL). By performing the extraction directly in a GC autosampler vial using one milliliter of contaminated water and one milliliter of organic solvent, then injecting a large volume (~150 mL) of the organic layer taken directly from the vial by an autosampler, the same analysis can be done simpler, quicker, and with much less organic solvent (1 mL).
Master of Science

Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xvii, 104 p.; also includes graphics (some col.). Includes abstract and vita. Advisor: Andy Ward, Dept.of Food, Agricultural, and Biological Engineering. Includes bibliographical references (p. 100-104).

Нами було досліджено зміни органометричних параметрів серця молодих щурів при гіпоосмолярній гіпергідратації. Вони характеризуються рівномірним збільшенням маси всіх камер серця та переважним розширенням порожнини правого шлуночка.
Нами были исследованы изменения органометричних параметров сердца молодых крыс при гипоосмолярной гипергидратации. Они характеризуются равномерным увеличением массы всех камер сердца и преимущественным расширением полости правого желудочка.
We studied the changes of organometric parameters of the heart in young rats at hypoosmolar overhydration. They are characterized by a uniform increase in the mass of all chambers of the heart and the preferential expansion of the cavity of the right ventricle.

Нами вивчалися oрганометричні параметри серця у молодих щурів при гіпоосмолярній гіпергідратації. Встановлено, що у молодих щурів під впливом гіпоосмолярної гіпергідратації збільшується маса усіх камер серця та переважно розширюється порожнина правого шлуночка.
Нами изучались органометрические параметры сердца у молодых крыс при гипоосмолярной гипергидратации. Установлено, что у молодых крыс под влиянием гипоосмолярной гипергидратации увеличивается масса всех камер сердца и преимущественно расширяется полость правого желудочка.
We studied the organometric parameters of the heart in young rats at hypoosmolar overhydration. We found, that morphometric data of young rats under the influence of hypoosmolar overhydration show a uniform increasing in the mass of all chambers of the heart and the preferential expansion of the cavity of the right ventricle.

Le sondeur multifaisceaux (SMF) est l'une des technologies d'acoustique sous-marine les plus avancées pour l'étude des fonds et de la colonne d'eau. Il requiert une réelle expertise pour son déploiement sur le terrain ainsi que pour l'élaboration de cartographies à partir des différentes données acquises. Ces traitements sont souvent chronophages en raison de la quantité de données acquises et demandent à être automatisés pour alléger le travail à l'hydrographe. C'est ce sur quoi portent les travaux réalisés durant cette thèse. Après des rappels sur des notions d'acoustique sous-marine, le fonctionnement du SMF est décrit et les types de données manipulées tout au long des traitements sont présentés. Le manuscrit s'articule ensuite autour de deux thématiques ˸ la cartographie bathymétrique et la cartographie biocénotique. Les développements sont intégrés dans les logiciels de l'entreprise Seaviews pour laquelle les travaux sont réalisés. Ils répondent à des besoins particuliers de l'entreprise.En ce qui concerne la cartographie bathymétrique, la donnée bathymétrique doit être préalablement triée pour écarter les sondes aberrantes et éviter qu'elles ne pénalisent la précision topographique. Ce tri d'innombrables sondes est une tâche que réalisent les hydrographes, assistés aujourd'hui d'outils numériques. Nous proposerons une méthode statistique rapide pour trier les sondes tout en réalisant une carte de profondeurs marines. Ce qui amène à se demander si les images de la colonne d'eau acquises également par le sondeur ne seraient pas exploitables pour déduire une bathymétrie exempte d'aberration. Nous testerons cette hypothèse à l'aide de l'apprentissage profond (deep learning) et en particulier par des réseaux de neurones convolutifs qui ont permis des progrès considérables en vision par ordinateur. La cartographie des habitats marins (les biocénoses) est un travail de classification de la nature des fonds à partir des données acoustiques du SMF en concordance avec les espèces vivant sur les lieux. La société Seaviews a développé une méthode de préparation des données SMF pour l'analyse des habitats. Nous nous orientons vers des méthodes de classification des habitats, à partir de ces données, par des techniques d'apprentissage automatique (machine learning). Plusieurs méthodes sont mises en place et testées, puis une zone d'étude est choisie pour évaluer et comparer les résultats des différentes approches
Among underwater acoustic technologies, multibeam echo sounder (MBES) is one of the most advanced tool to study and map the underwater floors and the above water column. Its deployment on-site requires expertise so as the whole data processing to map the information. These processing are very time-consuming due to the massive quantity of recorded data and thus needs to be automatised to shorten and alleviate the hydrographer's task. This PhD research works focus on the automatisation of the current activities in Seaviews society.After some reminders on the underwater acoustic sciences, the MBES operating is described as well the produced data that will be manipulated throughout the developments. This document presents two thematics˸ bathymetric (depths) and marine habitats mapping. The developments are integrated into the Seaviews' software in the aim to be used by all the employees.About seafloor depths mapping, the bathymetric sounding has to be sorted to avoid that the outlier errors distort the results. Sorting the uncountable measures is cumbersome but necessary, although the hydrographers are today happily computed-assisted. We propose a fast statistical method to exclude the outliers while mapping the information. This leads to wonder if the water column imagery would be workable to deduce the bathymetry without failure. We will test this hypothesis with some technics of deep learning, especially with convolutional neural networks.The marine habitats mapping is a seabed nature classification according to the local life. Seaviews has worked on a way to prepare MBES data and habitats analysis. Concerning the method of classification itself, we move towards machine learning technics. Several methods are implemented and assessed, and then an area is chosen to evaluate and compare the results

The fresh water resources of the world are stressed due to the increasing population. Theclimate change has also affected the water resource availability due to the occurrence offrequent and uneven extreme events such as drought and flash floods. In the context ofPeloponnese, Greece water resource management is an important issue for tourism developmentas well as the water supply for the people in the peninsula. To assess the potential climatechange and to quantify the water resource availability linear regression trend analysis andhydrological modeling has been done in this study. The hydro-climatic data (Temperature,precipitation, evapotranspiration and precipitation surplus) show a decreasing trend when a longstudy period (1951-2008) is considered; however, all the trends are not statistically significantexcept precipitation, actual evapotranspiration and precipitation surplus. Similarly, the case isquite opposite when IPCC standard period (1961-1990) is considered. In this period,precipitation and precipitation surplus is increasing but not statistically significant, whereastemperature and potential evapotranspiration has decreasing and statistically significant trendand actual evapotranspiration is decreasing but not statistically significant. Hence, it cannot beconcluded that the climate has changed in the peninsula with reference to linear regressionanalysis. On the other hand, it should be noted that the water resource availability will decreasein the peninsula if the current trend in the hydro-climatic data continues. Furthermore, a spatialanalysis shows that water availability is less in the eastern part and the coastal area of thepeninsula due to low precipitation and high evapotranspiration. Hence, these areas need to befocused on for the better water resource management and planning. However, the uncertaintiesrelated to data and model should be accounted for in the water resource management andplanning.

The study of radiative transfer in multifractal clouds is of great interest, an important application being to Global Climate Models. In this work we develop a formalism analogous to the multifractal singularity formalism for understanding photon scattering statistics in radiative transfer in multifractals, and test the results numerically on lognormal multifractals. Although the results are only exactly valid in the thick cloud limit, the approximation is found to be quite accurate down to optical thickness of $ tau approx1$-10, so the results may be widely applicable. Furthermore we show the possibility of "renormalizing" the multifractal by replacing it with a near equivalent homogeneous medium but with a "renormalized" optical thickness $ tau sp{1/(1+C sb1)}$ where C$ sb1$ is the codimension of the mean singularity of the cloud. We argue that this approximation is likely to continue to be valid for multiple scattering, and is also compatible with recent results for diffusion on multifractals. Finally we analyze cloud liquid water content data and estimate the universal multifractal indices. We find that the scaling is respected over the whole range 5m-330km and that the cloud can in fact be reasonably described by a lognormal multifractal.

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Esta dissertação teve como objetivo principal verificar a aplicabilidade da metodologia de análise de envoltória de dados - DEA (Data Envelopment Analysis) - ao setor de saneamento, avaliando a eficiência das 71 maiores empresas prestadoras de serviços de água e esgoto no Brasil, segundo o número de ligações ativas de água. Adicionalmente, foi gerada uma ordenação das empresas segundo suas eficiências relativas e procurou-se indicar o aumento mínimo necessário, em cada tipo de produto, que as empresas consideradas ineficientes teriam que realizar para se tornarem eficientes. O trabalho apresenta um breve histórico do setor de saneamento no País, desde o início das primeiras empresas até o projeto atual de modernização. Descrevem-se também os conceitos e modelos básicos da DEA, necessários para entendimento do texto. A DEA foi aplicada aos dados do Sistema Nacional de Informações sobre Saneamento. Os resultados são interpretados buscando-se ressaltar o potencial da metodologia para obtenção de indicadores de desempenho úteis para órgãos formuladores de políticas públicas e agências de financiamento. Análises adicionais foram feitas buscando testar a validade dos modelos, detectar a presença de economias de escala e avaliar a robustez dos resultados. Concluiu-se que a metodologia pode ser adequadamente empregada para análise do setor e auxiliar as políticas de concessão de financiamentos, oferecendo indicações iniciais para o estabelecimento de metas para melhorias.
This thesis seeks to test the applicability of the DEA (Data Envelopment Analysis) methodology for assessing the managerial efficiency of Brazilian water and sewage companies. This objective was pursued by evaluating the efficiency of the 71 largest companies of water services in Brazil, in terms of the number of properties served. Additionally, the companies were ranked according to their relative efficiency, and for each inefficient company it was estimated how much increase in each one of the outputs would be necessary for rendering it efficient. The text presents a brief history of the sanitation sector in Brazil, and reviews the basic DEA concepts and models necessary for understanding the analyses.The DEA was performed on the data collected by the Sistema Nacional de Informações sobre Saneamento (Brazilian National Sanitation Information System). The results were interpreted stressing the potential of DEA for generating performance indicators useful for public policies and funding agencies. Further analyses were made in order to test the validity of the model, detect likely economies of scale and to assess the robustness of the results.The results suggest that DEA can be adequate for analyzing the target industry, and for contributing to public policy formation and funding decisions by offering relevant indicators for setting improvement goals.

Uncertainty associated with the potential impact of climate change on supply availability, varied success with demand-side interventions such as water efficiency and changes in priority relating to hydrometric data collection and ownership, have resulted in challenges for water resources system management particularly in data-sparse regions. Consequently, the aim of this thesis is to assess the reliability, resilience and sustainability of water resources systems in both data-rich and data-sparse regions with an emphasis on robust decision-making in data-sparse regions. To achieve this aim, new resilience indicators that capture water resources system failure duration and extent of failure (i.e. failure magnitude) from a social and environmental perspective were developed. These performance indicators enabled a comprehensive assessment of a number of performance enhancing interventions, which resulted in the identification of a set of intervention strategies that showed potential to improve reliability, resilience and sustainability in the case studies examined. Finally, a multi-criteria decision analysis supported trade-off decision making when the reliability, resilience and sustainability indicators were considered in combination. Two case studies were considered in this research: Kingston and St. Andrew in Jamaica and Anyplace in the UK. The Kingston and St. Andrew case study represents the main data-sparse case study where many assumptions were introduced to fill data gaps. The intervention strategy that showed great potential to improve reliability, resilience and sustainability identified from Kingston and St. Andrew water resources assessment was the ‘Site A-east’ desalination scheme. To ameliorate uncertainty and lack of confidence associated with results, a methodology was developed that transformed a key proportion of the Anyplace water resources system from a data-rich environment to a data-sparse environment. The Anyplace water resources system was then assessed in a data-sparse environment and the performance trade-offs of the intervention strategies were analysed using four multi-criteria decision analysis (MCDA) weighting combinations. The MCDA facilitated a robust comparison of the interventions’ performances in the data-rich and data-sparse case studies. Comparisons showed consistency in the performances of the interventions across data-rich and data-sparse hydrological conditions and serve to demonstrate to decision makers a novel approach to addressing uncertainty when many assumptions have been introduced in the water resources management process due to data sparsity.

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Dissertacao (Mestrado)
IPEN/D
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

High-quality drinking water and wastewater systems are essential to public health, business, and quality of life in the United States. Even though the current performance of these systems is moderate, the concern is about the future performance. Planning can be done for improvement once the current performance of utilities is evaluated, and areas with a scope of improvement are identified. Benchmarking and performance evaluation are key components in the process of continuous improvement for utility\'s performance. Benchmarking helps utilities make policies and programmatic decisions that reduce operational expenses and increase productivity by understanding areas of underperformance, understanding customer needs, developing future plans, and setting goals. This study establishes a strong case for implementing benchmarking methodologies among utilities to evaluate and improve performance.
There are many initiatives on performance benchmarking of utilities but a few of them focuses on one or few area of performance. There are a few initiatives which use subjective indicators. Additionally, consultants visit the utilities for performance evaluation. This research focuses on creating a web-based benchmarking platform for performance evaluation using holistic and quantitative indicators. Practical and robust methodologies are used and the research presents the current performance comparisons among utilities for areas that impact overall utility\'s performance. Web based benchmarking consists of two major parts -- data collection and result visualization. A major contribution from this study is the creation of an online performance benchmarking database. With time more data will be collected which will provide utilities an access to a better database for performance evaluation. The future work in this research will be analyzing the data and results for each participant for each set of indicators, and finding possible reasons for under performance, followed by suggesting solutions for improvement using the best practices.

Master of Science

Water injection has been commonly used to maintain reservoir pressure and improve oil recovery in many oil fields. The equilibrium between reservoir rock and formation brine at initial reservoir conditions would be disturbed by the introduction of a non-native water, which mixes and reacts with the formation brine and interacts with minerals present in the formation. A series of brine/brine and/or brine/rock interactions would then take place in the flow paths from the injection well to the production well, potentially leading to dissolution and/or precipitation of minerals; knowledge of these interactions is crucial for the evaluation and the management of oilfield scale problems. In this thesis, thermodynamic models, reactive transport models and reservoir simulation models are used to identify the geochemical reactions occurring in the reservoir and investigate how the reservoir interactions affect the produced water composition. Brine composition data have been collected from 26 fields, and examples from four selective fields provide the basis for the analysis in this thesis. For Field X, a typical sandstone reservoir located in the North Sea region, a thermodynamic prediction model was used to calculate the risk of scale precipitation based on a series of individual produced water samples, thus providing an evaluation of the actual scaling risk in these samples, which is then compared with the usual theoretical estimate based on endpoint formation and injection brine compositions, and the erroneous assumption that no reactions in the reservoir impact the produced water composition. The occurrence of barium sulphate precipitation and calcium magnesium ion exchange reaction are identified by the modelling results. The Cation Exchange Capacity was identified as a modest 0.05 mol/kgw (50 meq/L) for this field. Since ion exchange capacity is an important parameter for some chemical EOR method, this a promising technique for EOR evaluation. An available history matched streamline reservoir simulation model of the Miller Field was then integrated with produced water chemical data. Streamline simulation is applied to better model brine mixing through reducing the numerical dispersion which cannot be effectively controlled in finite difference simulation. A simplified model of barite scale precipitation was included in the streamline simulation, and the calculation results with and without considering barite precipitation were compared with the observed produced water chemical data. The streamline simulation model assumes scale deposition is possible everywhere in the formation, whereas in reality the near production well zones were generally protected by squeezed scale inhibitor, and thus the discrepancies between modelled and observed barium concentrations at these two given wells diagnose the effectiveness of the chemical treatments to prevent formation. 1D and 2D reactive transport models were developed to identify the geochemical reactions occurring in the Gyda Field where there is a high reservoir temperature and formation water is high salinity. Anhydrite and barite precipitation are identified as the two dominant mineral reactions taking place deep within the reservoir. Anhydrite is deposited due to mixing of formation and injection waters in the area before this zone is cooled, and the precipitated anhydrite is gradually dissolved as the local reservoir temperature is lowered by cool injection water. The dissolved anhydrite then re-precipitates downstream in the at high temperature zones since the propagation of the temperature front is much slower than the brine mixing front. This creates a risk of late life anhydrite deposition in the producer. Finally, a carbonate reservoir study was performed for Ekofisk field where seawater flooding has been implemented. The 1D reactive transport model provides a good match with observed produced water chemistry data when the primary calcite mineral phase, calcium magnesium carbonate precipitation, temperature change and initial source of CO2 were modelled. In Ekofisk Field, calcite dissolution drives anhydrite and calcium magnesium carbonate precipitation. The modelled combination of calcium magnesium carbonate precipitation and ion exchange remove magnesium from the brine, also as observed from the produced water data. Simulation results also demonstrate that calcite dissolves quickly at first due to CO2 partitioning from the hydrocarbon phase into the brine. It was also shown that calcite dissolution is promoted by an increase in sulphate concentration in the injection water due to the coupled anhydrite precipitation. This body of work develop a methodology for systematically storing and analyzing produced brine data, and using modelling tools to identify what geochemical reactions have taken place. The methodology is then applied to various reservoir scenarios, leading to insights that impact scale management in these systems, and may also have a bearing on chemical EOR methods.

Les données acquises lors des surveillances environnementales peuvent être sujettes à différents types d'anomalies (i.e., données incomplètes, inconsistantes, inexactes ou aberrantes). Ces anomalies qui entachent la qualité des données environnementales peuvent avoir de graves conséquences lors de l'interprétation des résultats et l’évaluation des écosystèmes. Le choix des méthodes de prétraitement des données est alors crucial pour la validité des résultats d'analyses statistiques et il est assez mal défini. Pour étudier cette question, la thèse s'est concentrée sur l’acquisition des données et sur les protocoles de prétraitement des données afin de garantir la validité des résultats d'analyse des données, notamment dans le but de recommander la séquence de tâches de prétraitement la plus adaptée. Nous proposons de maîtriser l'intégralité du processus de production des données, de leur collecte sur le terrain et à leur analyse, et dans le cas de l'évaluation de la qualité de l'eau, il s’agit des étapes d'analyse chimique et hydrobiologique des échantillons produisant ainsi les données qui ont été par la suite analysées par un ensemble de méthodes statistiques et de fouille de données. En particulier, les contributions multidisciplinaires de la thèse sont : (1) en chimie de l'eau: une procédure méthodologique permettant de déterminer les quantités de pesticides organochlorés dans des échantillons d'eau collectés sur le terrain en utilisant les techniques SPE–GC-ECD (Solid Phase Extraction - Gas Chromatography - Electron Capture Detector) ; (2) en hydrobiologie : une procédure méthodologique pour évaluer la qualité de l’eau dans quatre rivières Mexicaines en utilisant des indicateurs biologiques basés sur des macroinvertébrés ; (3) en science des données : une méthode pour évaluer et guider le choix des procédures de prétraitement des données produites lors des deux précédentes étapes ainsi que leur analyse ; et enfin, (4) le développement d’un environnement analytique intégré sous la forme d’une application développée en R pour l’analyse statistique des données environnementales en général et l’analyse de la qualité de l’eau en particulier. Enfin, nous avons appliqué nos propositions sur le cas spécifique de l’évaluation de la qualité de l’eau des rivières Mexicaines Tula, Tamazula, Humaya et Culiacan dans le cadre de cette thèse qui a été menée en partie au Mexique et en France
Data obtained from environmental surveys may be prone to have different anomalies (i.e., incomplete, inconsistent, inaccurate or outlying data). These anomalies affect the quality of environmental data and can have considerable consequences when assessing environmental ecosystems. Selection of data preprocessing procedures is crucial to validate the results of statistical analysis however, such selection is badly defined. To address this question, the thesis focused on data acquisition and data preprocessing protocols in order to ensure the validity of the results of data analysis mainly, to recommend the most suitable sequence of preprocessing tasks. We propose to control every step in the data production process, from their collection on the field to their analysis. In the case of water quality assessment, it comes to the steps of chemical and hydrobiological analysis of samples producing data that were subsequently analyzed by a set of statistical and data mining methods. The multidisciplinary contributions of the thesis are: (1) in environmental chemistry: a methodological procedure to determine the content of organochlorine pesticides in water samples using the SPE-GC-ECD (Solid Phase Extraction – Gas Chromatography – Electron Capture Detector) techniques; (2) in hydrobiology: a methodological procedure to assess the quality of water on four Mexican rivers using macroinvertebrates-based biological indices; (3) in data sciences: a method to assess and guide on the selection of preprocessing procedures for data produced from the two previous steps as well as their analysis; and (4) the development of a fully integrated analytics environment in R for statistical analysis of environmental data in general, and for water quality data analytics, in particular. Finally, within the context of this thesis that was developed between Mexico and France, we have applied our methodological approaches on the specific case of water quality assessment of the Mexican rivers Tula, Tamazula, Humaya and Culiacan

Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.
Title from electronic title page (viewed Apr. 25, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Environmental Sciences and Engineering, School of Public Health." Discipline: Environmental Sciences and Engineering; Department/School: Public Health.

[EN] Over time, due to multiple operational and maintenance activities, the networks of water supply systems (WSSs) undergo interventions, modifications or even are closed. In many cases, these activities are not properly registered. Knowledge of the paths and characteristics (status and age, etc.) of the WSS pipes is obviously necessary for efficient and dynamic management of such systems. This problem is greatly augmented by considering the detection and control of leaks. Access to reliable leakage information is a complex task. In many cases, leaks are detected when the damage is already considerable, which brings high social and economic costs. In this sense, non-destructive methods (e.g., ground penetrating radar - GPR) may be a constructive response to these problems, since they allow, as evidenced in this thesis, to ascertain paths of pipes, identify component characteristics, and detect primordial water leaks. Selection of GPR in this work is justified by its characteristics as non-destructive technique that allows studying both metallic and non-metallic objects. Although the capture of information with GPR is usually successful, such aspects as the capture settings, the large volume of generated information, and the use and interpretation of such information require high level of skill and experience. This dissertation may be seen as a step forward towards the development of tools able to tackle the problem of lack of knowledge on the WSS buried assets. The main objective of this doctoral work is thus to generate tools and assess their feasibility of application to the characterization of components of WSSs from GPR images. In this work we have carried out laboratory tests specifically designed to propose, develop and evaluate methods for the characterization of the WSS buried components. Additionally, we have conducted field tests, which have enabled us to determine the feasibility of implementing such methodologies under uncontrolled conditions. The methodologies developed are based on techniques of intelligent data analysis. The basic principle of this work has involved the processing of data obtained through the GPR to look for useful information about WSS components, with special emphasis on the pipes. After performing numerous activities, one can conclude that, using GPR images, it is feasible to obtain more information than the typical identification of hyperbolae currently performed. In addition, this information can be observed directly, e.g. more simply, using the methodologies proposed in this doctoral work. These methodologies also prove that it is feasible to identify patterns (especially with the preprocessing algorithm termed Agent race) that provide fairly good approximation of the location of leaks in WSSs. Also, in the case of pipes, one can obtain such other characteristics as diameter and material. The main outcomes of this thesis consist in a series of tools we have developed to locate, identify and visualize WSS components from GPR images. Most interestingly, the data are synthesized and reduced so that the characteristics of the different components of the images recorded in GPR are preserved. The ultimate goal is that the developed tools facilitate decision-making in the technical management of WSSs, and that such tools can even be operated by personnel with limited experience in handling non-destructive methodologies, specifically GPR.
[ES] Con el paso del tiempo, y debido a múltiples actividades operacionales y de mantenimiento, las redes de los sistemas de abastecimiento de agua (SAAs) sufren intervenciones, modificaciones o incluso, son clausuradas, sin que, en muchos casos, estas actividades sean correctamente registradas. El conocimiento de los trazados y características (estado y edad, entre otros) de las tuberías en los SAAs es obviamente necesario para una gestión eficiente y dinámica de tales sistemas. A esta problemática se suma la detección y el control de las fugas de agua. El acceso a información fiable sobre las fugas es una tarea compleja. En muchos casos, las fugas son detectadas cuando los daños en la red son ya considerables, lo que trae consigo altos costes sociales y económicos. En este sentido, los métodos no destructivos (por ejemplo, ground penetrating radar - GPR), pueden ser una respuesta a estas problemáticas, ya que permiten, como se pone de manifiesto en esta tesis, localizar los trazados de las tuberías, identificar características de los componentes y detectar las fugas de agua cuando aún no son significativas. La selección del GPR, en este trabajo se justifica por sus características como técnica no destructiva, que permite estudiar tanto objetos metálicos como no metálicos. Aunque la captura de información con GPR suele ser exitosa, la configuración de la captura, el gran volumen de información, y el uso y la interpretación de la información requieren de alto nivel de habilidad y experiencia por parte del personal. Esta tesis doctoral se plantea como un avance hacia el desarrollo de herramientas que permitan responder a la problemática del desconocimiento de los activos enterrados de los SAAs. El objetivo principal de este trabajo doctoral es, pues, generar herramientas y evaluar la viabilidad de su aplicación en la caracterización de componentes de un SAA, a partir de imágenes GPR. En este trabajo hemos realizado ensayos de laboratorio específicamente diseñados para plantear, elaborar y evaluar metodologías para la caracterización de los componentes enterrados de los SAAs. Adicionalmente, hemos realizado ensayos de campo, que han permitido determinar la viabilidad de aplicación de tales metodologías bajo condiciones no controladas. Las metodologías elaboradas están basadas en técnicas de análisis inteligentes de datos. El principio básico de este trabajo ha consistido en el tratamiento adecuado de los datos obtenidos mediante el GPR, a fin de buscar información de utilidad para los SAAs respecto a sus componentes, con especial énfasis en las tuberías. Tras la realización de múltiples actividades, se puede concluir que es viable obtener más información de las imágenes de GPR que la que actualmente se obtiene con la típica identificación de hipérbolas. Esta información, además, puede ser observada directamente, de manera más sencilla, mediante las metodologías planteadas en este trabajo doctoral. Con estas metodologías se ha probado que también es viable la identificación de patrones (especialmente el pre-procesado con el algoritmo Agent race) que proporcionan aproximación bastante acertada de la localización de las fugas de agua en los SAAs. También, en el caso de las tuberías, se puede obtener otro tipo de características tales como el diámetro y el material. Como resultado de esta tesis se han desarrollado una serie de herramientas que permiten visualizar, identificar y localizar componentes de los SAAs a partir de imágenes de GPR. El resultado más interesante es que los resultados obtenidos son sintetizados y reducidos de manera que preservan las características de los diferentes componentes registrados en las imágenes de GPR. El objetivo último es que las herramientas desarrolladas faciliten la toma de decisiones en la gestión técnica de los SAAs y que tales herramientas puedan ser operadas incluso por personal con una experiencia limitada en el manejo
[CAT] Amb el temps, a causa de les múltiples activitats d'operació i manteniment, les xarxes de sistemes d'abastament d'aigua (SAAs) se sotmeten a intervencions, modificacions o fins i tot estan tancades. En molts casos, aquestes activitats no estan degudament registrats. El coneixement dels camins i característiques (estat i edat, etc.) de les canonades d'aigua i sanejament fa evident la necessitat d'una gestió eficient i dinàmica d'aquests sistemes. Aquest problema es veu augmentat en gran mesura tenint en compte la detecció i control de fuites. L'accés a informació fiable sobre les fuites és una tasca complexa. En molts casos, les fugues es detecten quan el dany ja és considerable, el que porta costos socials i econòmics. En aquest sentit, els mètodes no destructius (per exemple, ground penetrating radar - GPR) poden ser una resposta constructiva a aquests problemes, ja que permeten, com s'evidencia en aquesta tesi, per determinar rutes de canonades, identificar les característiques dels components, i detectar les fuites d'aigua quan encara no són significatives. La selecció del GPR en aquest treball es justifica per les seves característiques com a tècnica no destructiva que permet estudiar tant objectes metàl·lics i no metàl·lics. Tot i que la captura d'informació amb GPR sol ser reeixida, aspectes com ara la configuració de captura, el gran volum d'informació que es genera, i l'ús i la interpretació d'aquesta informació requereix alt nivell d'habilitat i experiència. Aquesta tesi pot ser vista com un pas endavant cap al desenvolupament d'eines capaces d'abordar el problema de la manca de coneixement sobre els actius d'aigua i sanejament enterrat. L'objectiu principal d'aquest treball doctoral és, doncs, generar eines i avaluar la seva factibilitat d'aplicació a la caracterització dels components de los SAAs, a partir d'imatges GPR. En aquest treball s'han dut a terme proves de laboratori específicament dissenyats per proposar, desenvolupar i avaluar mètodes per a la caracterització dels components d'aigua i sanejament soterrat. A més, hem dut a terme proves de camp, que ens han permès determinar la viabilitat de la implementació d'aquestes metodologies en condicions no controlades. Les metodologies desenvolupades es basen en tècniques d'anàlisi intel·ligent de dades. El principi bàsic d'aquest treball ha consistit en el tractament de dades obtingudes a través del GPR per buscar informació útil sobre els components d'SAA, amb especial èmfasi en la canonades. Després de realitzar nombroses activitats, es pot concloure que, amb l'ús d'imatges de GPR, és factible obtenir més informació que la identificació típica d'hipèrboles realitzat actualment. A més, aquesta informació pot ser observada directament, per exemple, més simplement, utilitzant les metodologies proposades en aquest treball doctoral. Aquestes metodologies també demostren que és factible per identificar patrons (especialment el pre-processat amb l'algoritme Agent race) que proporcionen bastant bona aproximació de la localització de fuites en SAAs. També, en el cas de tubs, es pot obtenir altres característiques com ara el diàmetre i el material. Els principals resultats d'aquesta tesi consisteixen en una sèrie d'eines que hem desenvolupat per localitzar, identificar i visualitzar els components dels SAAS a partir d'imatges GPR. El resultat més interessant és que els resultats obtinguts són sintetitzats i reduïts de manera que preserven les característiques dels diferents components registrats en les imatges de GPR. L'objectiu final és que les eines desenvolupades faciliten la presa de decisions en la gestió tècnica de SAA, i que tals eines poden fins i tot ser operades per personal amb poca experiència en el maneig de metodologies no destructives, específicament GPR.
Ayala Cabrera, D. (2015). Characterization of components of water supply systems from GPR images and tools of intelligent data analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59235
TESIS
Premiado

This study starts from the perspective of a future increase availability of water quality data at the water treatment facility at Lovön and aims to use the existing data to identify a pattern in the role of the different sub-basin that constitute the Mälaren basin. The data are analyzed with the graphical tool of the scatterplot and a Non-linear Canonical Correlation Analysis, a variation of the classical multivariate method, that by using a neural network model is able to handle not linear relationships. From the data analysis, it is possible to identify that different areas have different contribution in shaping the water quality at the facility of Lovön, but also that this pattern of contribution is strongly affected by the season inside the analyzed year.

New high-throughput behavioral systems enable the recording of continuous behavioral sequences with an unprecedented richness of signals and a deep temporal resolution. Automated systems offer neuroscience the opportunity to tackle in a new way the old question of how the brain orchestrates behavior and ultimately understand brain function itself, however, they accumulate large amounts of data leading to what is being termed Big Behavioral Data. The manipulation, analysis and contextualization of these data to obtain useful biological insights is not a trivial problem. This thesis presents Pergola, a computational framework to comprehensively analyze spontaneous longitudinal behaviors. Pergola provides access to a large set of mature genomic bioinformatics tools for the analysis and visualization of continuous behavioral recordings. I also explored multidimensional analysis techniques to help reducing the huge spatio-temporal dimensionality derived from behavioral recordings, and the high variability associated to all behavioral paradigms. This problem is addressed adapting Principal Component Analysis (PCA) for statistical inference on complex behaviors such as the recognition of learning strategies.
Els nous sistemes d’alt rendiment per l’estudi del comportament permeten el enregistrement de senyals continues de comportament amb una riquesa de senyals i una resolució temporal sense precedents. Els sistemes automàtics ofereixen a la neurociència la oportunitat d’abordar d’una nova manera la vella qüestió de com el cervell orquestra el comportament i finalment entendre la pròpia funció cerebral, però a la vegada acumulen grans quantitats de dades, el que s’ha vingut a anomenar Big Behavioral Data. La manipulació, anàlisis i contextualització d’aquestes enormes quantitats de dates per a obtenir coneixements biològics útils no és un problema trivial. Aquesta tesi presenta Pergola, un marc computacional per analitzar exhaustivament els comportaments espontanis longitudinals. Pèrgola ofereix accés a un ampli conjunt d'eines madures de la bioinformàtica genòmica que poden ser usades per a l'anàlisi i visualització d'enregistraments contínues de comportament. També he explorat tècniques d'anàlisi multidimensionals per ajudar a reduir l'enorme dimensió espai-temporal derivada dels enregistraments de comportament, i l'alta variabilitat associada a tots els paradigmes de comportament. He adreçat aquest problema mitjançant l'Anàlisi de Components Principals (PCA) per la inferència estadística de comportaments complexos com per exemple, el reconeixement de les estratègies d'aprenentatge.

I evaluated twenty-eight years (1980-2007) of spatial-temporal water quality data from Deer Creek Reservoir in Utah. The data came from three sampling points representing the lotic, transitional and lentic zones. The data included measurements of climatological, hydrological and water quality conditions at four depths; Surface, Above Thermocline, Below Thermocline and Bottom. The time frame spanned dates before and after the completion of the Jordanelle Reservoir (1987-1992), approximately fourteen miles upstream of Deer Creek. I compared temporal groupings and found that a traditional month distribution following standard seasons was not effective in characterizing the measured conditions; I developed a more representative seasonal grouping by performing a Tukey-Kramer multiple comparisons adjustment and a Bonferronian correction of the Student's t comparison. Based on these analyses, I determined the best groupings were Cold (December - April), Semi-Cold (May and November), Semi-Warm (June and October), Warm (July and September) and Transition (August). I performed principal component analysis (PCA) and factor analysis (FA) to determine principal parameters associated with the variability of the water quality of the reservoir. These parameters confirmed our seasonal groups showing the Cold, Transition and Warm seasons as distinct groups. The PCA and FA showed that the variables that drive most of the variability in the reservoir are specific conductivity and variables related with temperature. The PCA and FA showed that the reservoir is highly variable. The first 3 principal components and rotated factors explained a cumulative 59% and 47%, respectively of the variability in Deer Creek. Both parametric and nonparametric approaches provided similar correlations but the evaluations that included censored data (nutrients) were considerably different with the nonparametric approach being preferred.

Water trees are a major form of degradation in solid organic electrical insulation subject to high AC voltages and water. The work is aimed at developing a more rigorous approach to analysing water tree data from ageing experiments on practical insulation geometries. Such data is in the form of tree length distributions and time-increasing tree number densities. Tree inception statistics are directly accessible from the data, but the effects of growth are convolved with those of inception. An approach is developed for analysing the data to quantify aspects of both inception and growth. In particular, mean growth rates and distributions of growth times can be estimated. The distribution of inception times seems to be close to exponential. Analysis shows that the effects of varying the field on the dynamics of inception depend upon whether the voltage or the insulation thickness is being varied. Increasing the frequency or decreasing thickness increases the number of possible water tree sites but decreases the inception rate from an average site. Frequency accelerates inception in a non-linear manner. Increasing the voltage both increases the number of sites and the inception rates. At frequencies close to 1 kHz, the mean length of a tree increases with the square root of growth time. Initial tree growth rates increase in a way that is consistent with a linear dependence on frequency. It is concluded that the approach developed can be applied to real data and is useful. It is expected that application of the approach to more extensive data sets would give rise to considerable advances in the empirical knowledge of the dependence of water treeing on various physical parameters which it is not possible to obtain using existing techniques.

An evaluation of the geochemistry of the environment prior to large-scale changes enables scientists and other stakeholders to assess both baseline conditions and the potential impact of those changes to the environment. One area in which documentation of pre-development geochemistry is particularly important is in the exploitation of ore deposits. Ore deposits consist of concentrations of elements or minerals that are enriched enough to be of potential economic value. Their unusual geochemistry often leaves a signature on the environment that can both aid in location an economic resource and present environmental management challenges during its lifecycle. Coles Hill, Virginia, represents one such site. The Coles Hill property is the location of uranium-enriched rock, commonly referred to as the Coles Hill uranium deposit. This dissertation outlines study design, sampling, and statistical analysis methods that can be used in the geochemical characterization of a potential resource extraction site. It presents three studies on geoenvironmental media at Coles Hill. The first study discusses sampling strategies and statistical analysis to address variability in geology, hydrology and climate for baseline assessment and presents an example of such an assessment at Coles Hill. Results suggest a localized environmental impact of the deposit but that differences in bedrock geology within the area surrounding the deposit could also be responsible for some of the variation. This study also emphasizes the importance of consideration of data below analytical detection limits and describes methods for doing so. The second study compares the geochemistry of soil samples collected at Coles Hill with reference data collected by the U.S. Geological Survey using multivariate statistical techniques. Differences are used to suggest potential pathfinder elements such as light rare earth elements to aid in exploration for similar deposits. The third study uses multivariate statistical analysis to examine differences among rocks, soils, and stream sediments to infer important geochemical processes involved in weathering of the deposit. Overall, the results of these studies can aid in the development of future environmental site studies at Coles Hill and elsewhere.
Ph. D.

Sharks of the Atlantic coast of the United States have suffered increased fishing pressure in last three decades. Commercial and recreational catches jumped in the mid to late 1980's, leading to regulation by the National Marine Fisheries Service in the early 1990's. The Virginia Institute of Marine Sciences' shark long-line survey, begun in 1974, and continuing to the present day, is thus well positioned to look at the effects of this fishing mortality on sharks. Using GAM modelling, six of ten shark species analyzed, including the most common species, Carcharhinus plumbeus, suffered declines of from 98-99% of early abundances in the survey. Only two species showed no significant trends, and only one (C. obscurus) showed signs of recovery. Analysis of size changes showed that both C. plumbeus and C. obscurus have suffered declines in both mean and variance of their size distribution since 1974. Analyses of mass changes showed that five of thirteen species have shown biologically significant declines in mass per shark since 1974. Six of the remaining eight showed no trend in mass per shark. Habitat analyses showed that few species showed effects of climate scale variables such as the North Atlantic Oscillation index, Chesapeake Bay discharge, or the Palmer Drought Index. Many species showed significant changes in patterns in abundance with local environmental variables, such as temperature, salinity, and water depth. These patterns, when combined, revealed several groupings of species, including deep-water species, Bay-abundant species, and near-shore species. Another group consisted of species that occur in this area only as they move north and south en route to more northerly areas for summer months. One group was made up of two species (S. acanthias and M. canis) that occurred almost exclusively in cold water (April and May). Analysis of New Jersey long-line data from 1961-62 with a resample of many of the same sites revealed that abundances off New Jersey show a trend in both mass and abundance similar to that found in the VIMS survey data. Overall, this study demonstrated many trends in shark distribution and ecology not previously shown in any way other than anecdotally.

Managing water in urban areas is an ever increasingly complex challenge. Technology enables sustainable urban water management and with integrated smart metering solutions, massive amounts of water consumption data from the end users can be collected. However, the possibility of generating data from the end user holds no value in itself. It is with the use of data analysis the vast amount of the collected data can provide more insightful information creating potential benefits. It is recognized that a deeper understanding of the end user could potentially provide benefits for operational managers as well as for the end users. A single case study of a data set containing high frequency end user water consumption data from rental apartments has been conducted, where the data set was analyzed in order to see what possible information that could be extracted and interpreted based on an exploratory data analysis (EDA). Furthermore, an interview with the operational manager of the buildings under study as well as a literature review have been carried out in order to understand how the gathered data is used today and to which contexts it could be extrapolated to provide potential benefits at a building level. The results suggests that the EDA is a powerful method approach when starting out without strong preconception of the data under study and have successfully revealed patterns and a fundamental understanding of the data and its structure. Through analysis, variations over time, water consumption patterns and excessive water users have been identified as well as a leak identification process. Even more challenging than to make meaning of the data is to trigger actions, decisions and measures based on the data analysis. The unveiled information could be applied for an improved operational building management, to empower the customers, for business and campaign opportunities as well as for an integrated decision support system. To summarize, it is concluded that the usage of smart water metering data holds an untapped opportunity to save water, energy as well as money. In the drive towards a more sustainable and smarter city, smart water meter data from end users have the potential to enable smarter building management as well as smarter water services.

Extensive deforestations, particularly in the (sub)tropics, have led to intense soil degradation and erosion with concomitant reduction in soil fertility. Reforestations or plantations on those degraded sites may provide effective measures to mitigate further soil degradation and erosion, and can lead to improved soil quality. However, a change in land use from, e.g., grassland to forest may have a crucial impact on water balance. This may affect water availability even under humid tropical climate conditions where water is normally not a limiting factor. In this context, it should also be considered that according to climate change projections rainfall may decrease in some of these regions. To mitigate climate change related problems (e.g. increases in erosion and drought), reforestations are often carried out. Unfortunately, those measures are seldom completely successful, because the environmental conditions and the plant specific requirements are not appropriately taken into account. This is often due to data-scarcity and limited financial resources in tropical regions. For this reason, innovative approaches are required that are able to measure environmental conditions quasi-continuously in a cost-effective manner. Simultaneously, reforestation measures should be accompanied by monitoring in order to evaluate reforestation success and to mitigate, or at least to reduce, potential problems associated with reforestation (e.g. water scarcity). To avoid reforestation failure and negative implications on ecosystem services, it is crucial to get insights into the water balance of the actual ecosystem, and potential changes resulting from reforestation. The identification and prediction of water balance changes as a result of reforestation under climate change requires the consideration of the complex feedback system of processes in the soil-vegetation-atmosphere continuum. Models that account for those feedback system are Soil-Vegetation-Atmosphere-Transfer (SVAT) models. For the before-mentioned reasons, this study targeted two main objectives: (i) to develop and test a method combination for site evaluation under data scarcity (i.e. study requirements) (Part I) and (ii) to investigate the consequences of prediction uncertainty of the SVAT model input parameters, which were derived using geophysical methods, on SVAT modeling (Part II). A water balance modeling approach was set at the center of the site evaluation approach. This study used the one-dimensional CoupModel, which is a SVAT model. CoupModel requires detailed spatial soil information for (i) model parameterization, (ii) upscaling of model results and accounting for local to regional-scale soil heterogeneity, and (iii) monitoring of changes in soil properties and plant characteristics over time. Since traditional approaches to soil and vegetation sampling and monitoring are time consuming and expensive (and therefore often limited to point information), geophysical methods were used to overcome this spatial limitation. For this reason, vis-NIR spectroscopy (visible to near-infrared wavelength range) was applied for the measurement of soil properties (physical and chemical), and remote sensing to derive vegetation characteristics (i.e. leaf area index (LAI)). Since the estimated soil properties (mainly texture) could be used to parameterize a SVAT model, this study investigated the whole processing chain and related prediction uncertainty of soil texture and LAI, and their impact on CoupModel water balance prediction uncertainty. A greenhouse experiment with bamboo plants was carried out to determine plant-physiological characteristics needed for CoupModel parameterization. Geoelectrics was used to investigate soil layering, with the intent of determining site-representative soil profiles for model parameterization. Soil structure was investigated using image analysis techniques that allow the quantitative assessment and comparability of structural features. In order to meet the requirements of the selected study approach, the developed methodology was applied and tested for a site in NE-Brazil (which has low data availability) with a bamboo plantation as the test site and a secondary forest as the reference (reference site). Nevertheless, the objective of the thesis was not the concrete modeling of the case study site, but rather the evaluation of the suitability of the selected methods to evaluate sites for reforestations and to monitor their influence on the water balance as well as soil properties. The results (Part III) highlight that one needs to be aware of the measurement uncertainty related to SVAT model input parameters, so for instance the uncertainty of model input parameters such as soil texture and leaf area index influences meaningfully the simulated model water balance output. Furthermore, this work indicates that vis-NIR spectroscopy is a fast and cost-efficient method for soil measurement, mapping, and monitoring of soil physical (texture) and chemical (N, TOC, TIC, TC) properties, where the quality of soil prediction depends on the instrument (e.g. sensor resolution), the sample properties (i.e. chemistry), and the site characteristics (i.e. climate). Additionally, also the sensitivity of the CoupModel with respect to texture prediction uncertainty with respect to surface runoff, transpiration, evaporation, evapotranspiration, and soil water content depends on site conditions (i.e. climate and soil type). For this reason, it is recommended that SVAT model sensitivity analysis be carried out prior to field spectroscopic measurements to account for site specific climate and soil conditions. Nevertheless, mapping of the soil properties estimated via spectroscopy using kriging resulted in poor interpolation (i.e. weak variograms) results as a consequence of a summation of uncertainty arising from the method of field measurement to mapping (i.e. spectroscopic soil prediction, kriging error) and site-specific ‘small-scale’ heterogeneity. The selected soil evaluation method (vis-NIR spectroscopy, structure comparison using image analysis, traditional laboratory analysis) showed that there are significant differences between the bamboo soil and the adjacent secondary forest soil established on the same soil type (Vertisol). Reflecting on the major study results, it can be stated that the selected method combination is a way forward to a more detailed and efficient way to evaluate the suitability of a specific site for reforestation. The results of this study provide insights into where and when during soil and vegetation measurements a high measurement accuracy is required to minimize uncertainties in SVAT modeling
Umfangreiche Abholzungen, besonders in den (Sub-)Tropen, habe zu intensiver Bodendegradierung und Erosion mit einhergehendem Verlust der Bodenfruchtbarkeit geführt. Eine wirksame Maßnahme zur Vermeidung fortschreitender Bodendegradierung und Erosion sind Aufforstungen auf diesen Flächen, die bisweilen zu einer verbesserten Bodenqualität führen können. Eine Umwandlung von Grünland zu Wald kann jedoch einen entscheidenden Einfluss auf den Wasserhaushalt haben. Selbst unter humid-tropischen Klimabedingungen, wo Wasser in der Regel kein begrenzender Faktor ist, können sich Aufforstungen negativ auf die Wasserverfügbarkeit auswirken. In diesem Zusammenhang muss auch berücksichtigt werden, dass Klimamodelle eine Abnahme der Niederschläge in einigen dieser Regionen prognostizieren. Um die Probleme, die mit dem Klimawandel in Verbindung stehen zu mildern (z.B. Zunahme von Erosion und Dürreperioden), wurden und werden bereits umfangreiche Aufforstungsmaßnahmen durchgeführt. Viele dieser Maßnahmen waren nicht immer umfassend erfolgreich, weil die Umgebungsbedingungen sowie die pflanzenspezifischen Anforderungen nicht angemessen berücksichtigt wurden. Dies liegt häufig an der schlechten Datengrundlage sowie an den in vielen Entwicklungs- und Schwellenländern begrenzter verfügbarer finanzieller Mittel. Aus diesem Grund werden innovative Ansätze benötigt, die in der Lage sind quasi-kontinuierlich und kostengünstig die Standortbedingungen zu erfassen und zu bewerten. Gleichzeitig sollte eine Überwachung der Wiederaufforstungsmaßnahme erfolgen, um deren Erfolg zu bewerten und potentielle negative Effekte (z.B. Wasserknappheit) zu erkennen und diesen entgegenzuwirken bzw. reduzieren zu können. Um zu vermeiden, dass Wiederaufforstungen fehlschlagen oder negative Auswirkungen auf die Ökosystemdienstleistungen haben, ist es entscheidend, Kenntnisse vom tatsächlichen Wasserhaushalt des Ökosystems zu erhalten und Änderungen des Wasserhaushalts durch Wiederaufforstungen vorhersagen zu können. Die Ermittlung und Vorhersage von Wasserhaushaltsänderungen infolge einer Aufforstung unter Berücksichtigung des Klimawandels erfordert die Berücksichtigung komplex-verzahnter Rückkopplungsprozesse im Boden-Vegetations-Atmosphären Kontinuum. Hydrologische Modelle, die explizit den Einfluss der Vegetation auf den Wasserhaushalt untersuchen sind Soil-Vegetation-Atmosphere-Transfer (SVAT) Modelle. Die vorliegende Studie verfolgte zwei Hauptziele: (i) die Entwicklung und Erprobung einer Methodenkombination zur Standortbewertung unter Datenknappheit (d.h. Grundanforderung des Ansatzes) (Teil I) und (ii) die Untersuchung des Einflusses der mit geophysikalischen Methoden vorhergesagten SVAT-Modeleingangsparameter (d.h. Vorhersageunsicherheiten) auf die Modellierung (Teil II). Eine Wasserhaushaltsmodellierung wurde in den Mittelpunkt der Methodenkombination gesetzt. In dieser Studie wurde das 1D SVAT Model CoupModel verwendet. CoupModel benötigen detaillierte räumliche Bodeninformationen (i) zur Modellparametrisierung, (ii) zum Hochskalierung von Modellergebnissen unter Berücksichtigung lokaler und regionaler Bodenheterogenität, und (iii) zur Beobachtung (Monitoring) der zeitlichen Veränderungen des Bodens und der Vegetation. Traditionelle Ansätze zur Messung von Boden- und Vegetationseigenschaften und deren Monitoring sind jedoch zeitaufwendig, teuer und beschränken sich daher oft auf Punktinformationen. Ein vielversprechender Ansatz zur Überwindung der räumlichen Einschränkung sind die Nutzung geophysikalischer Methoden. Aus diesem Grund wurden vis-NIR Spektroskopie (sichtbarer bis nah-infraroter Wellenlängenbereich) zur quasi-kontinuierlichen Messung von physikalischer und chemischer Bodeneigenschaften und Satelliten-basierte Fernerkundung zur Ableitung von Vegetationscharakteristika (d.h. Blattflächenindex (BFI)) eingesetzt. Da die mit geophysikalisch hergeleiteten Bodenparameter (hier Bodenart) und Pflanzenparameter zur Parametrisierung eines SVAT Models verwendet werden können, wurde die gesamte Prozessierungskette und die damit verbundenen Unsicherheiten und deren potentiellen Auswirkungen auf die Wasserhaushaltsmodellierung mit CoupModel untersucht. Ein Gewächshausexperiment mit Bambuspflanzen wurde durchgeführt, um die zur CoupModel Parametrisierung notwendigen pflanzenphysio- logischen Parameter zu bestimmen. Geoelektrik wurde eingesetzt, um die Bodenschichtung der Untersuchungsfläche zu untersuchen und ein repräsentatives Bodenprofil zur Modellierung zu definieren. Die Bodenstruktur wurde unter Verwendung einer Bildanalysetechnik ausgewertet, die die qualitativen Bewertung und Vergleichbarkeit struktureller Merkmale ermöglicht. Um den Anforderungen des gewählten Standortbewertungsansatzes gerecht zu werden, wurde die Methodik auf einem Standort mit einer Bambusplantage und einem Sekundärregenwald (als Referenzfläche) in NO-Brasilien (d.h. geringe Datenverfügbarkeit) entwickelt und getestet. Das Ziel dieser Arbeit war jedoch nicht die Modellierung dieses konkreten Standortes, sondern die Bewertung der Eignung des gewählten Methodenansatzes zur Standortbewertung für Aufforstungen und deren zeitliche Beobachtung, als auch die Bewertung des Einfluss von Aufforstungen auf den Wasserhaushalt und die Bodenqualität. Die Ergebnisse (Teil III) verdeutlichen, dass es notwendig ist, sich den potentiellen Einfluss der Messunsicherheiten der SVAT Modelleingangsparameter auf die Modellierung bewusst zu sein. Beispielsweise zeigte sich, dass die Vorhersageunsicherheiten der Bodentextur und des BFI einen bedeutenden Einfluss auf die Wasserhaushaltsmodellierung mit CoupModel hatte. Die Arbeit zeigt weiterhin, dass vis-NIR Spektroskopie zur schnellen und kostengünstigen Messung, Kartierung und Überwachung boden-physikalischer (Bodenart) und -chemischer (N, TOC, TIC, TC) Eigenschaften geeignet ist. Die Qualität der Bodenvorhersage hängt vom Instrument (z.B. Sensorauflösung), den Probeneigenschaften (z.B. chemische Zusammensetzung) und den Standortmerkmalen (z.B. Klima) ab. Die Sensitivitätsanalyse mit CoupModel zeigte, dass der Einfluss der spektralen Bodenartvorhersageunsicherheiten auf den mit CoupModel simulierten Oberflächenabfluss, Evaporation, Transpiration und Evapotranspiration ebenfalls von den Standortbedingungen (z.B. Klima, Bodentyp) abhängt. Aus diesem Grund wird empfohlen eine SVAT Model Sensitivitätsanalyse vor der spektroskopischen Feldmessung von Bodenparametern durchzuführen, um die Standort-spezifischen Boden- und Klimabedingungen angemessen zu berücksichtigen. Die Anfertigung einer Bodenkarte unter Verwendung von Kriging führte zu schlechten Interpolationsergebnissen in Folge der Aufsummierung von Mess- und Schätzunsicherheiten (d.h. bei spektroskopischer Feldmessung, Kriging-Fehler) und der kleinskaligen Bodenheterogenität. Anhand des gewählten Bodenbewertungsansatzes (vis-NIR Spektroskopie, Strukturvergleich mit Bildanalysetechnik, traditionelle Laboranalysen) konnte gezeigt werden, dass es bei gleichem Bodentyp (Vertisol) signifikante Unterschiede zwischen den Böden unter Bambus und Sekundärwald gibt. Anhand der wichtigsten Ergebnisse kann festgehalten werden, dass die gewählte Methodenkombination zur detailreicheren und effizienteren Standortuntersuchung und -bewertung für Aufforstungen beitragen kann. Die Ergebnisse dieser Studie geben einen Einblick darauf, wo und wann bei Boden- und Vegetationsmessungen eine besonders hohe Messgenauigkeit erforderlich ist, um Unsicherheiten bei der SVAT Modellierung zu minimieren
Extensos desmatamentos que estão sendo feitos especialmente nos trópicos e sub-trópicos resultam em uma intensa degradação do solo e num aumento da erosão gerando assim uma redução na sua fertilidade. Reflorestamentos ou plantações nestas áreas degradadas podem ser medidas eficazes para atenuar esses problemas e levar a uma melhoria da qualidade do mesmo. No entanto, uma mudança no uso da terra, por exemplo de pastagem para floresta pode ter um impacto crucial no balanço hídrico e isso pode afetar a disponibilidade de água, mesmo sob condições de clima tropical úmido, onde a água normalmente não é um fator limitante. Devemos levar também em consideração que de acordo com projeções de mudanças climáticas, as precipitações em algumas dessas regiões também diminuirão agravando assim, ainda mais o quadro apresentado. Para mitigar esses problemas relacionados com as alterações climáticas, reflorestamentos são frequentemente realizados mas raramente são bem-sucedidos, pois condições ambientais como os requisitos específicos de cada espécie de planta, não são devidamente levados em consideração. Isso é muitas vezes devido, não só pela falta de dados, como também por recursos financeiros limitados, que são problemas comuns em regiões tropicais. Por esses motivos, são necessárias abordagens inovadoras que devam ser capazes de medir as condições ambientais quase continuamente e de maneira rentável. Simultaneamente com o reflorestamento, deve ser feita uma monitoração a fim de avaliar o sucesso da atividade e para prevenir, ou pelo menos, reduzir os problemas potenciais associados com o mesmo (por exemplo, a escassez de água). Para se evitar falhas e reduzir implicações negativas sobre os ecossistemas, é crucial obter percepções sobre o real balanço hídrico e as mudanças que seriam geradas por esse reflorestamento. Por este motivo, esta tese teve como objetivo desenvolver e testar uma combinação de métodos para avaliação de áreas adequadas para reflorestamento. Com esse intuito, foi colocada no centro da abordagem de avaliação a modelagem do balanço hídrico local, que permite a identificação e estimação de possíveis alterações causadas pelo reflorestamento sob mudança climática considerando o sistema complexo de realimentação e a interação de processos do continuum solo-vegetação-atmosfera. Esses modelos hidrológicos que investigam explicitamente a influência da vegetação no equilíbrio da água são conhecidos como modelos Solo-Vegetação-Atmosfera (SVAT). Esta pesquisa focou em dois objetivos principais: (i) desenvolvimento e teste de uma combinação de métodos para avaliação de áreas que sofrem com a escassez de dados (pré-requisito do estudo) (Parte I), e (ii) a investigação das consequências da incerteza nos parâmetros de entrada do modelo SVAT, provenientes de dados geofísicos, para modelagem hídrica (Parte II). A fim de satisfazer esses objetivos, o estudo foi feito no nordeste brasileiro,por representar uma área de grande escassez de dados, utilizando como base uma plantação de bambu e uma área de floresta secundária. Uma modelagem do balanço hídrico foi disposta no centro da metodologia para a avaliação de áreas. Este estudo utilizou o CoupModel que é um modelo SVAT unidimensional e que requer informações espaciais detalhadas do solo para (i) a parametrização do modelo, (ii) aumento da escala dos resultados da modelagem, considerando a heterogeneidade do solo de escala local para regional e (iii) o monitoramento de mudanças nas propriedades do solo e características da vegetação ao longo do tempo. Entretanto, as abordagens tradicionais para amostragem de solo e de vegetação e o monitoramento são demorados e caros e portanto muitas vezes limitadas a informações pontuais. Por esta razão, métodos geofísicos como a espectroscopia visível e infravermelho próximo (vis-NIR) e sensoriamento remoto foram utilizados respectivamente para a medição de propriedades físicas e químicas do solo e para derivar as características da vegetação baseado no índice da área foliar (IAF). Como as propriedades estimadas de solo (principalmente a textura) poderiam ser usadas para parametrizar um modelo SVAT, este estudo investigou toda a cadeia de processamento e as incertezas de previsão relacionadas à textura de solo e ao IAF. Além disso explorou o impacto destas incertezas criadas sobre a previsão do balanço hídrico simulado por CoupModel. O método geoelétrico foi aplicado para investigar a estratificação do solo visando a determinação de um perfil representante. Já a sua estrutura foi explorada usando uma técnica de análise de imagens que permitiu a avaliação quantitativa e a comparabilidade dos aspectos estruturais. Um experimento realizado em uma estufa com plantas de bambu (Bambusa vulgaris) foi criado a fim de determinar as caraterísticas fisiológicas desta espécie que posteriormente seriam utilizadas como parâmetros para o CoupModel. Os resultados do estudo (Parte III) destacam que é preciso estar consciente das incertezas relacionadas à medição de parâmetros de entrada do modelo SVAT. A incerteza presente em alguns parâmetros de entrada como por exemplo, textura de solo e o IAF influencia significantemente a modelagem do balanço hídrico. Mesmo assim, esta pesquisa indica que vis-NIR espectroscopia é um método rápido e economicamente viável para medir, mapear e monitorar as propriedades físicas (textura) e químicas (N, TOC, TIC, TC) do solo. A precisão da previsão dessas propriedades depende do tipo de instrumento (por exemplo da resolução do sensor), da propriedade da amostra (a composição química por exemplo) e das características das condições climáticas da área. Os resultados apontam também que a sensitividade do CoupModel à incerteza da previsão da textura de solo em respeito ao escoamento superficial, transpiração, evaporação, evapotranspiração e ao conteúdo de água no solo depende das condições gerais da área (por exemplo condições climáticas e tipo de solo). Por isso, é recomendado realizar uma análise de sensitividade do modelo SVAT prior a medição espectral do solo no campo, para poder considerar adequadamente as condições especificas do área em relação ao clima e ao solo. Além disso, o mapeamento de propriedades de solo previstas pela espectroscopia usando o kriging, resultou em interpolações de baixa qualidade (variogramas fracos) como consequência da acumulação de incertezas surgidas desde a medição no campo até o seu mapeamento (ou seja, previsão do solo via espectroscopia, erro do kriging) e heterogeneidade especifica de uma pequena escala. Osmétodos selecionados para avaliação das áreas (vis-NIR espectroscopia, comparação da estrutura de solo por meio de análise de imagens, análise de laboratório tradicionais) revelou a existência de diferenças significativas entre o solo sob bambu e o sob floresta secundária, apesar de ambas terem sido estabelecidas no mesmo tipo de solo (vertissolo). Refletindo sobre os principais resultados do estudo, pode-se afirmar que a combinação dos métodos escolhidos e aplicados representam uma forma mais detalhada e eficaz de avaliar se uma determinada área é adequada para ser reflorestada. Os resultados apresentados fornecem percepções sobre onde e quando, durante a medição do solo e da vegetação, é necessário se ter uma precisão mais alta a fim de minimizar incertezas potenciais na modelagem com o modelo SVAT

This study explores shallow lake numerical hydrodynamic processes that support model development and validation, extreme events and effects of water circulation in Lake Victoria. Lake Victoria is the second largest freshwater lake in the world, and the largest in East Africa. It is the major freshwater reservoir and source for domestic, agriculture, industrial, fishery, and transport. The resources support livelihoods and ecosystem services for over 40 million people. The lake is severely affected by water quality degradation by pollution. This thesis aims at improving the understanding by following recommendation of the Lake Victoria Environment Management Project, Lake Victoria Basin Commission climate change adaptation strategy and action plan 2018-2023, Lake Victoria Basin Commission operational plan 2015-2020, and Lake Victoria Basin Commission report. These reports suggested detailed lake bathymetry survey, modelling of lake flow, study of lake hydrometeorological processes by modelling and simulation, to identify extreme weather events, assess water circulation effect, and study lake pollution near the shore. A numerical hydrodynamic model was built in the COMSOL Multiphysics (CM) software for assessing lake flows and water turn-over from river inflows which carry pollution. The work included the development of systematic methods for lake bathymetry that are relevant for lake numerical and hydrodynamic modelling. The hydrometeorological driven simulation model was employed to assess lake water balance, water circulation and soluble transport. Paper 1 creates a bathymetry from several methods and from several data sources, and a vertically integrated free surface flow model was implemented in CM. The model was used to investigate outflow conditions, mean velocities driven by river inflow, outflow, precipitation and evaporation. It is shown to be exactly conservative and give water level variation in reasonable agreement with measurements. The results indicate that the shallow water model is close to linear. An outflow model, linear in water level, predicts water level reasonable agreement with measurements. The findings suggest that the model should consider wind stress driven flow to provide more accurate lake flow behavior. Paper 2 performed an assessment of the hydro-meteorological processes and extreme weather events that are responsible for changing the characteristics of lake water balance, and changing streamflow variations, and lake transportation. We compare historical data over a long time with data from the model including water balance, sources of data uncertainty, correlations, extreme rain and inflow years, and seasonal variations. Solute loading and transportation was illustrated by tracing the water from the river inflows. The results indicate that the lake rainfall has a strong seasonal variation with strong correlations between tributary inflows and precipitation, and between lake outflow and water level. The tracer transport by mean flow is very slow. Flow increases somewhat in wet periods and is faster in the shallow Kenya lake zone than in the deeper Uganda and Tanzanian lake zones, where the major inflow, from the Kagera River, appears to strongly influence transportation.
Denna studie undersöker med numerisk metodik hydrodynamiska processer i den mycket grundaVictoriasjön och hur de påverkas av extrem väderlek, inflöden, och nederbörd. Victoriasjön är denandra största sötvattensjön i världen, och den största i Afrika. Den är färskvattenförråd och källa förhushåll, jordbruk, industri, fiske och transporter. Resurserna ger livsuppehåll och ekosystemtjänsterför mer än 40 miljoner människor. Sjön är utsatt för allvarliga föroreningar som försämrarvattenkvaliteten. Detta arbete avser att förbättra förståelsen genom att följa rekommendationer somgivits ut av Lake Victoria Environment Management Project (LVEMP), och Lake Victoria BasinCommissions (LVBC) rapporter om strategi för anpassning till klimatförändringar, åtgärdsplan2018-2023 och översiktsplan 2015-2020. Rapporterna föreslår detaljerad genomgång avdjupkartor, modellering av strömning i sjön i syfte att identifiera extrema väderhändelser,undersöka vattencirkulationen, och studera föroreningarna nära stränder. En hydrodynamisknumerisk modell har byggts i simuleringspaketet COMSOL Multiphysics (CM) för uppskattning avströmning och vattenutbyte från förorenade inflöden. Arbetet innefattade utveckling av metoder förvattendjups-modeller för hydrodynamiska studier. Simuleringsmodellen drivs avhydrometeorologiska data och används för vattenmängds-balans, cirkulation ochföroreningstransport.Artikel 1 skapar vattendjupskartan från flera data-mängder med olika metoder. En vertikaltintegrerad modell med fri yta implementerades i CM. Modellen ger vertikalt medelvärdesbildadehastigheter drivna av flodinflöden, utflöde, nederbörd och avdunstning. Modellen representerarvattenbalansen exakt och ger variationer i vattennivå i rimlig överensstämmelse med mätningar.Resultaten antyder att modellen är nära linjär och tids-invariant. En utflödesmodell ansatt somlinjär i vatten-nivån kan anpassas noggrant till historiska data. Bättre realism kan uppnås omvindens pådrivande verkan inkluderas.Artikel 2 går igenom de hydro-meteorologiska processer och extrema väder-händelser som ändrarvattenbalans, strömningsmönster och transport. Vi har jämfört data över femtio år med modellens,inkluderande vattennivå, källor för osäkerhet i data, korrelationer, år med extrema regn ochinflöden, och årstidsvariationer. Resultaten tyder på att nederbörden varierar kraftigt medårstiderna, och signifikanta korrelationer ses mellan nederbörd och inflöden, och mellan utflöde ochvattennivå.Transport av lösliga föroreningar illustrerades genom spårning av vatten från de olika inflödena.Spårämnestransport med vertikalt medelvärdesbildade hastigheter är mycket långsam.Strömningen ökar något i våta årstider och är snabbare i den grunda zonen i Kenya än i de djuparedelarna i Uganda och Tanzania. Det största inflödet som kommer från Kagera tycks ha stor inverkanpå transporten.

QC 20191106

Nutrient inputs into the environment greatly impact urban ecosystems. Appropriate management strategies are needed to limit eutrophication of surface water bodies and contamination of groundwater. In many existing urban environments, retrofits or complete upgrades are needed for stormwater and/or wastewater infrastructure to manage nutrients. However, sustainable urban nutrient management requires comprehensive baseline data that is often not available. A Framework for Urban Nutrient (FUN) Management for Geographic Information Systems (GIS) was developed to specifically address those areas with limited data access. Using spatial analysis in GIS, it links water quality, land use, and socio-demographics, thereby reducing data collection and field-based surveying efforts. It also presents preliminary results in a visually accessible format, potentially improving how data is shared and discussed amongst diverse stakeholders. This framework was applied to two case studies, one in Orange County Florida and one in Placencia, Belize. A stormwater pond index (SPI) was developed to evaluate 961 residential wet ponds in Orange County, Florida where data was available for land use and socio-demographic parameters, but limited for water quality. The SPI consisted of three categories (recreation, aesthetics, education) with a total of 13 indicators and provided a way to score the cultural and ecosystem services of 41 ponds based on available data. Using only three indicators (presence of a fence, Dissolved Oxygen (DO) < 4 mg/l, and water depth < 3 ft), 371 out of 961 stormwater ponds were assessed. Additional criteria based on socio-demographic information (distance to a school, population density, median household income under $50,000, percentage of population below the poverty line, and distance to parks) identified seven wet ponds as optimum for potential intervention to benefit residents and urban nutrient management purposes. For the second case study, a water quality analysis and impact assessment was performed for the Placencia peninsula and lagoon in Belize. This study had access to water quality data, but limited land use data and very limited socio-demographic data. Since May 2014, water quality samples have been taken from 56 locations and analyzed monthly. For this study, Dissolved Oxygen (DO), Nitrate (NO3--N), Ammonia (NH3), Chemical Oxygen Demand (COD), and 5-Day Biochemical Oxygen Demand (BOD5), Escherichia coli (E. coli), and Enterococci were selected to assess spatial and temporal variation of water quality in the groundwater on the peninsula as well as the surface water in lagoon, estuaries and along the coast. A spline interpolation of DO, Nitrate, BOD5, and COD for June 2016 indicated the concentration distribution of those parameters and areas of special concern. A spatial analysis was conducted that showed that Nitrate and Enterococci exceeded the effluent limits of Belize very frequently in the complete study area while the other parameters contributed to the identification of key areas of concern. As a high variability of concentrations over time was observed, a temporal analysis was conducted identifying a link between the water quality data and two temporal impact factors, rainfall and tourism. The two case studies showed the broad and flexible application of the FUN management for GIS and the great advantages the use of GIS offers to reduce costs and resources use.

Despite emerging water shortages, most water is only used once, and often with low efficiency. However, with appropriate treatment, water can be re–used to reduce the demand on freshwater sources. The Department of Water Affairs, South Africa, promotes industries to reduce discharges into water resources in order to sustain an overall good water quality of all water systems. All of this ultimately leads to industries striving towards zero effluent discharge. Primary Column Bottoms (PCBs) is a wastewater stream derived from the Fischer–Tropsch Gas to Liquid process and consists mainly of organic acids, but no nitrogen or phosphorous, which by implication excludes possible biodegradation. In the operation of cooling towers in industrial processes, cooling water quality has a direct impact on the cooling performance of the system, where nutrient levels may affect fouling, scaling and corrosion observed in the cooling towers. Fouling, scaling and corrosion affect the operating efficiency of cooling water systems and may necessitate the addition of chemical agents to control these phenomena. This has a financial and labour time impact on the operation of these systems. In this study a mini cooling tower test rig was operated with a synthetic PCB effluent as cooling water and various cycles of concentration, pH and linear flow velocities (LFVs). A constant delta temperature of 10 °C was maintained. Cycles of concentration (COC) evaluated included 2, 4 and 6 cycles of concentration and linear flow velocities evaluated was 0.6 m/s, 0.9 m/s and 1.2 m/s. Fouling, scaling and corrosion rates were determined using corrosion coupons and heat exchanger tubes for mild steel and stainless steel. Besides the evaluation of the various operational parameters for fouling, scaling and corrosion, the possibility for chemical oxygen demand (COD) removal by operating the cooling tower as a bioreactor was also evaluated. To this end nutrient correction was applied to the reactor to allow for a CNP ratio of 100:10:1. With regard to fouling, scaling and corrosion, mild steel was more affected by fouling, scaling and corrosion compared to stainless steel where almost no fouling, scaling and corrosion was observed. Overall increased linear flow velocities resulted in higher fouling and scaling rates, whereas lower linear flow velocities resulted in decreased corrosion rates. In terms of cycles of concentration, increased COC resulted in higher fouling, scaling and corrosion rates. Despite the high nutrient removal levels, the accompanying fouling, scaling and corrosion was still below the particular industry’s guidelines. Besides physical–chemical evaluation of the towers under the various operational conditions, culture–dependent and culture–independent methods were also employed. Concerning culture–dependent approaches the study demonstrated that aerobic and anaerobic organisms are present in both the planktonic and sessile phase of the cooling tower reactors. Heterotrophic aerobes were found to be the most abundant under all the operating conditions. Sulphate reducing bacteria were more abundant in the sessile phase of the cooling towers, and the presence of high sulphate levels in the experiments could be indicative of the sulphate reducing bacteria actively participating in the microbial community. Lower than expected corrosion levels, however, suggest that a combination of the organisms in the biofilm rather than sulphate reducing bacteria alone, contributed to the corrosion rates observed. Culture–independent methods, specifically phospholipid fatty acid analysis supported the results from the culture–dependent methods. Furthermore results demonstrated that linear flow velocity had a greater effect on the community structure than cycles of concentration. Finally molecular methods, specifically denaturing gradient gel electrophoresis, found that increasing cycles of concentration resulted in increased microbial community diversity, while increasing linear flow velocity resulted in decreased microbial community diversity. Regarding COD removal, nutrient correction of the synthetic PCB effluent achieved 89.35 % COD removal at 2 COC and 1.2 m/s LFV, while 80.85 % COD removal was achieved at 4 COC at 1.2 m/s LFV. From these results it was recommended that the operation of the cooling tower should be at 4 COC and 1.2 m/s, which despite slightly lower % COD removal, were characterised by fouling, scaling and corrosion rates well within guidelines.
Thesis (M. Environmental Science)--North-West University, Potchefstroom Campus, 2012.

Computerized data visualization and analysis tools, especially Geographic Information Systems (GIS), constitute an important part of today&
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s water resources development and management studies. In order to obtain satisfactory results from such tools, accurate and comprehensive hydrography datasets are needed that include both spatial and hydrologic information on surface water resources and watersheds. If present, such datasets may support many applications, such as hydrologic and environmental modeling, impact assessment, and construction planning. The primary purposes of this study are production of prototype national hydrography and watershed datasets for Turkey, and development of GIS-based tools for the analysis of local water quality and quantity data. For these purposes national hydrography datasets and analysis systems of several counties are reviewed, and based on gained experience
1) Sub-watershed boundaries of 26 major national basins are derived from digital elevation model of the country by using raster-based analysis methods and these watersheds are named according to coding system of the European Union, 2) A prototype hydrography dataset with built-in connectivity and water flow direction information is produced from publicly available data sources, 3) GIS based spatial tools are developed to facilitate navigation through streams and watersheds in the hydrography dataset, and 4) A state-of-the art GIS-based stream flow and water quality data analysis system is developed, which is based on the structure of nationally available data and includes advanced statistical and spatial analysis capabilities. All datasets and developed tools are gathered in a single graphical user-interface within GIS and made available to the end-users.

This study is an attempt to uncover the underlying operation and maintenance issues in sustaining the operation of an automated green building with focus on its water systems. The Grove Library is a public building in Peppermint Grove, Western Australia, with smart design aimed to address environmentally sustainable concepts in their entirety. A sophisticated Building Management System (BMS) integrates the operation of the entire Environmentally Sustainable Design (ESD) systems in operation at The Grove. However a number of continual breakdowns in the operation of the ESD water systems, namely wastewater, rainwater and irrigation systems, lead to high levels of disappointment for the people managing and operating these systems. This is the first time that data have been extracted from the BMS. Prior to this study, it was possible to quantify the performance of any of the ESD water systems. This has resulted in the inability of external contractors to monitor the ESD systems’ operation. This study discussed the factors contributing to this situation using data analysis as the main method for troubleshooting and investigating the causes to these breakdowns. Upon achieving a good understanding of the situation, the recommendations were able to be formulated. It was found that the current situation with the ESD water systems at The Grove is a consequence of some interrelated factors forming a ‘vicious circle’. The ESD systems, their maintenance, the BMS and the management were the key factors identified as responsible for the issues in The Grove. The recommendations produced in this study were mainly on the technical side of the problem, that is, the BMS, ESD water systems and their maintenance. In order to overcome the issues at The Grove this relationship among these factors should be recognised and established.

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Este trabalho apresenta uma análise de dados hidrográficos coletados em quatro campanhas oceanográficas do programa REVIZEE/SCORE-N. Estas campanhas foram realizadas durante os períodos de Março-Maio de 1995, Outubro-Novembro de 1997, Maio-Junho de 1999, e Julho-Agosto 2001, na área da Plataforma Continental do Amazonas e região oceânica adjacente. Esta área corresponde a Zona Econômica Exclusiva do Norte do Brasil (ZEE-N). As informações obtidas in situ foram complementadas com os resultados do Experimento ATL6 Projeto CLIPPER, realizado com a versão 8.1 do modelo numérico francês de circulação oceânica geral OPA. As análises dos dados hidrográficos permitiram descrever a variação sazonal e espacial das quatro massas de água encontradas na região: Água Costeira (AC), Água Tropical (AT), Água Central do Atlântico Sul (ACAS) e Água Intermediária da Antártica (AIA). Através das medidas observacionais e dos resultados numéricos foi possível avaliar o deslocamento da massa de água amazônica, que atingiu mais de 300 km de distância perpendicular à costa durante o período de máxima descarga do rio (Abril-Maio), mas que foi deslocada para Noroeste durante a época de baixas vazões do rio (Outubro-Novembro). Os resultados indicaram também que a ACAS sofre uma expansão de cerca de 70 m (49oW) e de 220 m (50oW) durante o período de transição das descargas fluviais, projetando-se sobre a plataforma. As análises permitiram identificar a descarga fluvial de águas doces, e o afluxo de águas sub-superficiais de máxima salinidade provenientes do Atlântico Sul, transportadas para a região pelo sistema Corrente Sul Equatorial (CSE)/Corrente Norte do Brasil (CNB)/Sub-corrente Norte do Brasil (SCNB), como os dois principais processos que contribuem para a formação das Camadas de Barreira (CB) na área de estudo. Durante Março-Maio, período correspondendo às altas descargas do rio Amazonas, foram observadas espessuras de CB da ordem de 50 m, governadas pelo estabelecimento de uma forte picnoclina induzida pela mudança brusca do gradiente de salinidade no interior da camada isotérmica. Na investigação dos principais mecanismos físicooceanográficos de conexão entre a ZEE-N e o Oceano Atlântico tropical, verificou-se a ocorrência sistemática de intrusão sub-superficial de águas do Atlântico Sul via SCNB, que após retroflexão (43ºW), alimenta as subcorrentes equatoriais (SCE e SCNE). As análises das estações localizadas mais ao Norte da região estudada (49ºW), e os resultados das simulações do Experimento ATL 6 mostraram a presença de águas provindas do Atlântico Norte na área de estudo, identificando a Sub-corrente de Fronteira Oeste (SCFO) (alimentada a 50ºW pela recirculação da CNE), como principal responsável pelo transporte das águas do Atlântico Norte para a ZEE-N

To facilitate robust understanding of the processes and properties that govern a groundwater system, managers need data. However, this often requires them to make difficult decisions about what types of data to collect and where and when to collect it in the most cost - effective manner. This is where data worth analysis, which is based on predictive uncertainty analyses, can play an important role. The ‘worth’ of data is defined here as the reduction in uncertainty of a specific prediction of interest that is achieved as a result of a given data collection strategy. With the use of data worth analysis, the optimal data types, sample locations, and sampling frequencies can be determined for a specific prediction that informs, for example, management decisions. In this study a data worth method was used to optimize data collection when predicting pumping - induced stream depletion (water quantity section) and when predicting changing nitrate concentrations as a result of land-use change (water quality section). Specifically, the First Order Second Moment (FOSM) based data worth method was employed. This thesis also builds upon previous work which explores the impacts of spatial model parameterisation on the performance of the data worth analysis in the context of stream depletion assessments. A transient groundwater model was developed, using the MODFLOW-NWT software, and a steady state transport model was developed, using the MT3D-USGS software for the mid-Mataura catchment located in Southland, New Zealand. The ‘worth’ of both existing and additional potential monitoring data were investigated. In addition, and for only the water quantity part of the thesis, three spatial hydraulic parameter density scenarios were investigated to assess of parameter simplification on the performance of the data worth method: 1) distributed pilot-point parameters, 2) homogeneous parameters, and 3) grid-cell based parameters. The water quantity (stream depletion) predictions were made at 2 key locations: (i) the catchment outlet at Gore and (ii) the outlet of a spring-fedstream (McKellar Stream). The water quality prediction (change in nitrate concentration due to land-use change) was made at 7 locations 4 key surface water locations, 2 town supply bores at Gore and one additional groundwater location further upstream. For the water quantity predictions, results show that the existing transient groundwater level data resulted in the largest reduction in uncertainty for the predictions examined. Because the low flow predictions at Gore were integrating predictions, the most uncertainty reducing observations were scattered through the catchment area with a focus on the north-west. This coincides with the recharge zone (which means that there are large water level fluctuations and hence a larger ‘signal to noise’ content in the groundwater level data). In contrast, because McKellar Stream is a discrete prediction (in this case, because McKellar Stream is spring-fed), the observations directly surrounding the stream reduce the uncertainty the most significantly. The impact of parameter simplification in the water quantity modelling showed that the data worth analysis using the grid-cell based parameterisation were very similar to those using pilot-points. However, when using the homogeneous parameterisation, the data worth results became corrupted by the lack of spatial variability available in the parameterisation. Indicating that spatial heterogeneity is needed when predicting low flows, as was shown by previous studies. However, the computational time associated with performing data worth uncertainty analyses is much higher with a grid-cell based parameterisation. A pilot-point based scheme should perhaps therefore be considered a favourable option. For the water quality predictions, results showed a strong correlation between the hydraulic conductivity, porosity and denitrification. This is likely because the hydraulic conductivity and porosity provide information about the velocity of the groundwater for a given hydraulic - head gradient, which provides information about the amount of time available for denitrification to take place in the soil substrate. Next to that, results showed no distinct difference between surface water and groundwater predictions when predicting changing nitrate concentrations, but they showed that the spatial data worth patterns depended on the proximity of the prediction location to the denitrifying areas. Overall it can be concluded that spatial parameterisation is needed when performing a data worth study for stream depletion predictions, however a more detailed parameterisation than pilot – points does not provide significantly more information. Next to that, it can be concluded that the spatial data worth patterns when predicting low flows mainly depend on if the predictions are integrating or discrete predictions. Lastly, it can also be concluded that the data worth patterns when predicting change in nitrate concentration depend on the proximity of the prediction location to the denitrifying areas.