Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management

2017 Summer.Includes bibliographical references.The majority of river basins in the world, have undergone a great deal of transformations in terms of infrastructure and water management practices in order to meet increasing water needs due to population growth and socio-economic development. Surface water and groundwater systems are interwoven with environmental and socio-economic ones. The systems' dynamic nature, their complex interlinkages and interdependencies are inducing challenges for integrated water resources management. Informed decision-making process in water resources is deriving from a systematic analysis of the available data with the utilization of tools and models, by examining viable alternatives and their associated tradeoffs under the prism of a set of prudent priorities and expert knowledge. In an era of increasing volume and variety of data about natural and anthropogenic systems, opportunities arise for further enhancing data integration in problem-solving approaches and thus support decision-making for water resources planning and management. Although there is a plethora of variables monitored in various spatial and temporal scales, particularly in the United States, in real life, for water resources applications there are rarely, if ever, perfect data. Developing more systematic procedures to integrate the available data and harness their full potential of generating information, will improve the understanding of water resources systems and assist at the same time integrated water resources management efforts. The overarching objective of this study is to develop tools and approaches to overcome data obstacles in water resources management. This required the development of methodologies that utilize a wide range of water and environmental datasets in order to transform them into reliable and valuable information, which would address unanswered questions about water systems and water management practices, contributing to implementable efforts of integrated water resources management. More specifically, the objectives of this research are targeted in three complementary topics: drought, water demand, and groundwater supply. In this regard, their unified thread is the common quest for integrated river basin management (IRBM) under changing water resources conditions. All proposed methodologies have a common area of application namely the South Platte basin, located within Colorado. The area is characterized by limited water resources with frequent drought intervals. A system's vulnerability to drought due to the different manifestations of the phenomenon (meteorological, agricultural, hydrological, socio-economic and ecological) and the plethora of factors affecting it (precipitation patterns, the supply and demand trends, the socioeconomic background etc.) necessitates an integrated approach for delineating its magnitude and spatiotemporal extent and impacts. Thus, the first objective was to develop an implementable drought management policy tool based on the standardized drought vulnerability index framework and expanding it in order to capture more of drought's multifaceted effects. This study illustrated the advantages of a more transparent data rigorous methodology, which minimizes the need for qualitative information replacing it with a more quantitative one. It is believed that such approach may convey drought information to decision makers in a holistic manner and at the same time avoid the existing practices of broken linkages and fragmentation of reported drought impacts. Secondly, a multi-scale (well, HUC-12, and county level) comparative analysis framework was developed to identify the characteristics of the emergent water demand for unconventional oil and gas development. This effort revealed the importance of local conditions in well development patterns that influence water demand, the magnitude of water consumption in local scales in comparison to other water uses, the strategies of handling flowback water, and the need for additional data, and improved data collection methods for a detailed water life-cycle analysis including the associated tradeoffs. Finally, a novel, easy to implement, and computationally low cost methodology was developed for filling gaps in groundwater level time series. The proposed framework consists of four main components, namely: groundwater level time series; data (groundwater level, recharge and pumping) from a regional physically-based groundwater flow model; autoregressive integrated moving average with external inputs modeling; and the Ensemble Smoother (ES) technique. The methodology's efficacy to predict accurately groundwater levels was tested by conducting three numerical experiments at eighteen alluvial wells. The results suggest that the framework could serve as a valuable tool in gaining further insight of alluvium aquifer dynamics by filling missing groundwater level data in an intermittent or continuous (with relative short span) fashion. Overall, it is believed that this research has important implications in water resources decision making by developing implementable frameworks which advance further the understanding of water systems and may aid in integrated river basin management efforts

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp

Land, Water, Infrastructure And People: Considerations Of Planning For Distributed Stormwater Management Systems

When urbanization occurs, the removal of vegetation, compaction of soil and construction of impervious surfaces—roofs, asphalt, and concrete—and drainage infrastructure result in drastic changes to the natural hydrological cycle. Stormwater runoff occurs when rain does not infiltrate into soil. Instead it ponds at the surface and forms shallow channels of overland flow. The result is increased peak flows and pollutant loads, eroded streambanks, and decreased biodiversity in aquatic habitat. In urban areas, runoff is typically directed into catch basins and underground pipe systems to prevent flooding, however such systems are also failing to meet modern environmental goals. Green infrastructure is the widely evocative idea that development practices and stormwater management infrastructure can do better to mimic the natural hydrological conditions through distributed vegetation and source control measures that prevent runoff from being produced in the first place. This dissertation uses statistics and high-resolution, coupled surface-subsurface hydrologic simulation (ParFlow.CLM) to examine three understudied aspects of green infrastructure planning. First, I examine how development characteristics affect the runoff response in urban catchments. I find that instead of focusing on site imperviousness, planners should aim to preserve the ecosystem functions of infiltration and evapotranspiration that are lost even with low density development. Second, I look at how the spatial configuration of green infrastructure at the neighborhood scale affects runoff generation. While spatial configuration of green infrastructure does result in statistically significant differences in performance, such differences are not likely to be detectable above noise levels present in empirical monitoring data. In this study, there was no evidence of reduced hydrological effectiveness for green infrastructure located at sag points in the topography. Lastly, using six years of empirical data from a voluntary residential green infrastructure program, I show how the spread of green infrastructure depends on the demographic and physical characteristics of neighborhoods as well as spatially-dependent social processes (such as the spread of information). This dissertation advances the science of green infrastructure planning at multiple scales and in multiple sectors to improve the practice of urban water resource management and sustainable development

An index-based risk assessment approach for accidental contaminant release from waste management facilities during flood events

Natural hazards can trigger technological disasters in installations such as waste management facilities, and chemical and processing plants, leading to explosions, fires, and/or the release of dangerous substances. The likelihood of these ‘Natech’ incidents can be exacerbated by climate change intensifying very wet weather patterns leading to floods. Rising sea levels may further contribute to heightened flood risk. Consequently, the number of ‘at risk’ installations to flooding is increasing, and this trend is expected to continue as our climate warms. The Seveso Directive provides guidelines for identifying installations handling dangerous substances and mandates safety measures to minimise the likelihood and impact of accidents. However, the risk of contaminant release during floods is not limited to installations falling under the Seveso Directive. Various small and medium-sized facilities, such as waste management facilities, often located near residential areas, also handle hazardous waste and pose a threat to both human health and the environment in the event of accidental release. This research assesses the vulnerability of waste management facilities to flooding. We use an adapted form of the Water Risk Index (WRI), originally designed for large-scale industrial facilities, to estimate risk of flooding to waste facilities on a facility-by-facility basis. The initial application of the WRI to waste management facilities revealed significant gaps such as the absence of detailed georeferenced areas representing the spatial extent of the waste management facilities, the neglect of the spatial context, and the lack of consideration for waste materials that can degrade into smaller particulates such as microplastics. Here we address these gaps to enhance the evaluation methods for understanding the impacts of flooding on waste management facilities and the potential consequences on the environment and community resilience. Three primary methodologies have been developed and tested in Great Britain (GB) to address the knowledge gaps. The first methodology determines the spatial extent of waste management facilities, providing a comprehensive understanding of their footprint. In testing their vulnerability to inundation, the results indicate that a decrease in flood likelihood corresponds to an increase in the number of affected waste management facilities and the severity of the impact. Specifically, out of the 1,049 facilities tested, 10% (23 sites) displayed more than 40% of their footprint at risk from high flood likelihood (with a 10% annual probability). These percentages rise to 33% (88 sites) and 35% (111 sites) for medium (0.5%) and low likelihoods (0.1%), respectively. The second methodology assesses the vulnerability of waste facilities to flooding at the national scale by considering contextual factors from physical and human geography. These factors form a new multi-index-based assessment considering hazard, vulnerability, and exposure. The aim was to identify hotspots that necessitate additional analysis at the local level to efficiently mitigate the risk. The overall risk index (categorised as low, medium, and high) is estimated for a total of 7,292 facilities across GB. Approximately 15% (1,094 sites) classified with a high-risk index are located in areas at high risk of pluvial flood likelihood. Medium and low flood risks increase these figures to 37% (2,697 sites) and 44% (3,204 sites), respectively. We show that facilities with a high-risk index outweigh those with medium and low risks, particularly in scenarios with a high likelihood of floods, whether fluvial or pluvial. These results indicate that for flood-affected waste management facilities, the vulnerability of receptors is frequently triggered at the full potential. Finally, the third methodology establishes a framework to assess the plastic mobilisation potential from waste management facilities by estimating the location and quantity of waste materials capable of releasing synthetic micro-components into floodwaters. The term Microplastic Releasers (MPRs) is introduced to describe waste materials capable of degrading into synthetic microplastic components. MPRs include plastic, synthetic textile, and rubber waste. When applying the method to waste management facilities across GB, the results indicate a significant amount of MPRs at high risk of fluvial flooding, totalling nearly 1 million tonnes. However, the impact of pluvial flooding is even more severe: in the case of flood events ranging from a 5-year to a 1,000-year return period, the exposure of MPRs to floodwaters increases tenfold, from 1 to 11 million tonnes. By integrating the methodologies developed in this research, hotspots for further research on risk management and mitigation at the local level can be identified. Stakeholders and policymakers may reconsider the placement of waste facilities to non-flood-prone areas. If relocation is not possible, mitigation measures such as the implementation of flood defences as well as site-specific containment systems designed to minimise the release of synthetic micro components during a flood event can be introduced. The results have significant implications not only for waste management practices but also for broader discussions on environmental management, risk assessment, and the resilience of industries in the face of climate change.Natural hazards can trigger technological disasters in installations such as waste management facilities, and chemical and processing plants, leading to explosions, fires, and/or the release of dangerous substances. The likelihood of these ‘Natech’ incidents can be exacerbated by climate change intensifying very wet weather patterns leading to floods. Rising sea levels may further contribute to heightened flood risk. Consequently, the number of ‘at risk’ installations to flooding is increasing, and this trend is expected to continue as our climate warms. The Seveso Directive provides guidelines for identifying installations handling dangerous substances and mandates safety measures to minimise the likelihood and impact of accidents. However, the risk of contaminant release during floods is not limited to installations falling under the Seveso Directive. Various small and medium-sized facilities, such as waste management facilities, often located near residential areas, also handle hazardous waste and pose a threat to both human health and the environment in the event of accidental release. This research assesses the vulnerability of waste management facilities to flooding. We use an adapted form of the Water Risk Index (WRI), originally designed for large-scale industrial facilities, to estimate risk of flooding to waste facilities on a facility-by-facility basis. The initial application of the WRI to waste management facilities revealed significant gaps such as the absence of detailed georeferenced areas representing the spatial extent of the waste management facilities, the neglect of the spatial context, and the lack of consideration for waste materials that can degrade into smaller particulates such as microplastics. Here we address these gaps to enhance the evaluation methods for understanding the impacts of flooding on waste management facilities and the potential consequences on the environment and community resilience. Three primary methodologies have been developed and tested in Great Britain (GB) to address the knowledge gaps. The first methodology determines the spatial extent of waste management facilities, providing a comprehensive understanding of their footprint. In testing their vulnerability to inundation, the results indicate that a decrease in flood likelihood corresponds to an increase in the number of affected waste management facilities and the severity of the impact. Specifically, out of the 1,049 facilities tested, 10% (23 sites) displayed more than 40% of their footprint at risk from high flood likelihood (with a 10% annual probability). These percentages rise to 33% (88 sites) and 35% (111 sites) for medium (0.5%) and low likelihoods (0.1%), respectively. The second methodology assesses the vulnerability of waste facilities to flooding at the national scale by considering contextual factors from physical and human geography. These factors form a new multi-index-based assessment considering hazard, vulnerability, and exposure. The aim was to identify hotspots that necessitate additional analysis at the local level to efficiently mitigate the risk. The overall risk index (categorised as low, medium, and high) is estimated for a total of 7,292 facilities across GB. Approximately 15% (1,094 sites) classified with a high-risk index are located in areas at high risk of pluvial flood likelihood. Medium and low flood risks increase these figures to 37% (2,697 sites) and 44% (3,204 sites), respectively. We show that facilities with a high-risk index outweigh those with medium and low risks, particularly in scenarios with a high likelihood of floods, whether fluvial or pluvial. These results indicate that for flood-affected waste management facilities, the vulnerability of receptors is frequently triggered at the full potential. Finally, the third methodology establishes a framework to assess the plastic mobilisation potential from waste management facilities by estimating the location and quantity of waste materials capable of releasing synthetic micro-components into floodwaters. The term Microplastic Releasers (MPRs) is introduced to describe waste materials capable of degrading into synthetic microplastic components. MPRs include plastic, synthetic textile, and rubber waste. When applying the method to waste management facilities across GB, the results indicate a significant amount of MPRs at high risk of fluvial flooding, totalling nearly 1 million tonnes. However, the impact of pluvial flooding is even more severe: in the case of flood events ranging from a 5-year to a 1,000-year return period, the exposure of MPRs to floodwaters increases tenfold, from 1 to 11 million tonnes. By integrating the methodologies developed in this research, hotspots for further research on risk management and mitigation at the local level can be identified. Stakeholders and policymakers may reconsider the placement of waste facilities to non-flood-prone areas. If relocation is not possible, mitigation measures such as the implementation of flood defences as well as site-specific containment systems designed to minimise the release of synthetic micro components during a flood event can be introduced. The results have significant implications not only for waste management practices but also for broader discussions on environmental management, risk assessment, and the resilience of industries in the face of climate change

Malaria in the Amazon: An Agent-Based Approach to Epidemiological Modeling of Coupled Systems

The epidemiology of malaria considers a complex set of local interactions amongst host, vector, and environment. A history of reemergence, epidemic transition, and ensuing endemic transmission in Iquitos, Peru reveals an interesting case used to model and explore such interactions. In this region of the Peruvian Amazon, climate change, development initiatives and landscape fragmentation are amongst a unique set of local spatial variables underlying the endemicity of malaria. Traditional population-based approaches lack the ability to resolve the spatial influences of these variables. Presented is a framework for spatially explicit, agent-based modeling of malaria transmission dynamics in Iquitos and surrounding areas. The use of an agent-based model presents a new opportunity to spatially define causal factors and influences of transmission between mosquito vectors and human hosts. In addition to spatial considerations, the ability to model individual decisions of humans can define socio-economic and human-environment interactions related to malaria transmission. Three interacting sub-models representing human decisions, vector dynamics, and environmental factors comprise the model. Feedbacks between the interacting sub-models define individual decisions and ultimately the flexibility that will allow the model to function in a diagnostic capacity. Sensitivity analysis and simulated interactions are used to discuss this diagnostic capability and to build understanding of the physical systems driving local transmission of malaria

Using Physical and Social Sensors in Real-Time Data Streaming for Natural Hazard Monitoring and Response

Technological breakthroughs in computing over the last few decades have resulted in important advances in natural hazards analysis. In particular, integration of a wide variety of information sources, including observations from spatially-referenced physical sensors and new social media sources, enables better estimates of real-time hazard. The main goal of this work is to utilize innovative streaming algorithms for improved real-time seismic hazard analysis by integrating different data sources and processing tools into cloud applications. In streaming algorithms, a sequence of items from physical and social sensors can be processed in as little as one pass with no need to store the data locally. Massive data volumes can be analyzed in near-real time with reasonable limits on storage space, an important advantage for natural hazard analysis. Seismic hazard maps are used by policymakers to set earthquake resistant construction standards, by insurance companies to set insurance rates and by civil engineers to estimate stability and damage potential. This research first focuses on improving probabilistic seismic hazard map production. The result is a series of maps for different frequency bands at significantly increased resolution with much lower latency time that includes a range of high-resolution sensitivity tests. Second, a method is developed for real-time earthquake intensity estimation using joint streaming analysis from physical and social sensors. Automatically calculated intensity estimates from physical sensors such as seismometers use empirical relationships between ground motion and intensity, while those from social sensors employ questionaries that evaluate ground shaking levels based on personal observations. Neither is always sufficiently precise and/or timely. Results demonstrate that joint processing can significantly reduce the response time to a damaging earthquake and estimate preliminary intensity levels during the first ten minutes after an event. The combination of social media and network sensor data, in conjunction with innovative computing algorithms, provides a new paradigm for real-time earthquake detection, facilitating rapid and inexpensive risk reduction. In particular, streaming algorithms are an efficient method that addresses three major problems in hazard estimation by improving resolution, decreasing processing latency to near real-time standards and providing more accurate results through the integration of multiple data sets

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

2012 GREAT Day Program

SUNY Geneseo’s Sixth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1006/thumbnail.jp