No description available.

Thesis advisor: Noah Snyder / Increasing attention has been drawn to the need for reliable streamflow estimates at ungaged locations under a range of climatic and hydrologic conditions. Climate projections for the northeastern United States over the 21st century--which include significant increases in temperature and precipitation--could have broad impacts on streamflows, potentially reducing the accuracies of existing streamflow models for the region. This thesis investigates recent changes in daily flow-durations in southern New England, and examines their influence on the reliability of the low-flow models for Massachusetts presented by Ries and Friesz (2000). An analysis of discharge data collected at gaging sites through water year 2012 revealed increases in nearly all flow durations at sites across southern New England since the mid-20th century, whereas very low flows (quantiles at or above the 95-percent exceedance probability) generally showed decreases, especially since the 1990s. Twenty-year moving streamflow quantiles at each of ten selected exceedance probabilities were examined for the periods of record of 16 streamflow-gaging stations in southern New England. The beginning of water year 1992 appeared to mark an inflection point in low-flow quantiles, before which very low flows were steady or increasing, and after which these flows showed near-universal decreases. While the observed peak in 20-year low-flow quantiles around 1992 may be due to the statistical method used to calculate the quantile trends, the inflection point could also be an indicator of when increasing evapotranspiration surpassed increasing precipitation as the principal climatic driver of changes in low flows in southern New England. The general upward translation of the flow-duration curve observed over the last 60 years is very likely linked to increases in annual precipitation during this period, while the decreases in very low flows are likely due to changes in climatic variables (increasing summer temperatures and evapotranspiration rates), and amplified by anthropogenic factors (greater areas of impervious surfaces and increasing rates of surface- and ground-water withdrawal). The data suggest that increasing precipitation rates have already caused the Ries and Friesz (2000) equations for the median low flows (Q50 to Q75) to become biased towards underestimation, and decreases in very low flows threaten to render the models for these flows biased towards overestimation in the coming decades. The streamflow quantile trends (for both the entire period of record of the gaging stations and just the post-1992 period) for each of the ten flow-durations of interest were extended into the future to the point where the corresponding Ries and Friesz (2000) model would fail (when actual flow durations would be outside the 90-percent prediction intervals for the estimated flows for greater than 10% of sites). The models for the lowest streamflows are estimated to lose validity by as early as 2018. Climate change is predicted to have significant effects on streamflow characteristics in southern New England over the 21st century, and the results of this study indicate that the Ries and Freisz (2000) low-flow models should be reformulated using more recent streamflow data within the next decade, and validated every 20 years thereafter to ensure their accuracies are maintained despite the effects of regional nonstationarity. / Thesis (MS) — Boston College, 2014. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Earth and Environmental Sciences.

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. This thesis aims to show a novel methodology that shows a way to close the research gap regarding the use of social networks as a proxy for precipitation-runoff and flood forecast estimates. To address this, it is proposed to use a transformation function that creates a proxy variable for rainfall by analysing messages from geo-social media and precipitation measurements from authoritative sources, which are then incorporated into a hydrological model for the flow estimation. Then the proxy and authoritative rainfall data are merged to be used in a data assimilation scheme using the Ensemble Kalman Filter (EnKF). It is found that the combined use of authoritative rainfall values with the social media proxy variable as input to the Probability Distributed Model (PDM), improves flow simulations for flood monitoring. In addition, it is found that when these models are made under a scheme of fusion-assimilation of data, the results improve even more, becoming a tool that can help in the monitoring of \"ungauged\" or \"poorly gauged\" catchments. The main contribution of this thesis is the creation of a completely original source of rain monitoring, which had not been explored in the literature in a quantitative way. It also shows how the joint use of this source and data assimilation methodologies aid to detect flood events. / As inundações são um dos tipos mais devastadores de desastres em todo o mundo em termos de perdas humanas, econômicas e sociais. Se os dados oficiais forem escassos ou indisponíveis por alguns períodos, outras fontes de informação são necessárias para melhorar a estimativa de vazões e antecipar avisos de inundação. Esta tese tem como objetivo mostrar uma metodologia que mostra uma maneira de fechar a lacuna de pesquisa em relação ao uso de redes sociais como uma proxy para as estimativas de precipitação e escoamento. Para resolver isso, propõe-se usar uma função de transformação que cria uma variável proxy para a precipitação, analisando mensagens de medições geo-sociais e precipitação de fontes oficiais, que são incorporadas em um modelo hidrológico para a estimativa de fluxo. Em seguida, os dados de proxy e precipitação oficial são fusionados para serem usados em um esquema de assimilação de dados usando o Ensemble Kalman Filter (EnKF). Descobriu-se que o uso combinado de valores oficiais de precipitação com a variável proxy das mídias sociais como entrada para o modelo distribuído de probabilidade (Probability Distributed Model - PDM) melhora as simulações de fluxo para o monitoramento de inundações. A principal contribuição desta tese é a criação de uma fonte completamente original de monitoramento de chuva, que não havia sido explorada na literatura de forma quantitativa.

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. This thesis aims to show a novel methodology that shows a way to close the research gap regarding the use of social networks as a proxy for precipitation-runoff and flood forecast estimates. To address this, it is proposed to use a transformation function that creates a proxy variable for rainfall by analysing messages from geo-social media and precipitation measurements from authoritative sources, which are then incorporated into a hydrological model for the flow estimation. Then the proxy and authoritative rainfall data are merged to be used in a data assimilation scheme using the Ensemble Kalman Filter (EnKF). It is found that the combined use of authoritative rainfall values with the social media proxy variable as input to the Probability Distributed Model (PDM), improves flow simulations for flood monitoring. In addition, it is found that when these models are made under a scheme of fusion-assimilation of data, the results improve even more, becoming a tool that can help in the monitoring of \"ungauged\" or \"poorly gauged\" catchments. The main contribution of this thesis is the creation of a completely original source of rain monitoring, which had not been explored in the literature in a quantitative way. It also shows how the joint use of this source and data assimilation methodologies aid to detect flood events. / As inundações são um dos tipos mais devastadores de desastres em todo o mundo em termos de perdas humanas, econômicas e sociais. Se os dados oficiais forem escassos ou indisponíveis por alguns períodos, outras fontes de informação são necessárias para melhorar a estimativa de vazões e antecipar avisos de inundação. Esta tese tem como objetivo mostrar uma metodologia que mostra uma maneira de fechar a lacuna de pesquisa em relação ao uso de redes sociais como uma proxy para as estimativas de precipitação e escoamento. Para resolver isso, propõe-se usar uma função de transformação que cria uma variável proxy para a precipitação, analisando mensagens de medições geo-sociais e precipitação de fontes oficiais, que são incorporadas em um modelo hidrológico para a estimativa de fluxo. Em seguida, os dados de proxy e precipitação oficial são fusionados para serem usados em um esquema de assimilação de dados usando o Ensemble Kalman Filter (EnKF). Descobriu-se que o uso combinado de valores oficiais de precipitação com a variável proxy das mídias sociais como entrada para o modelo distribuído de probabilidade (Probability Distributed Model - PDM) melhora as simulações de fluxo para o monitoramento de inundações. A principal contribuição desta tese é a criação de uma fonte completamente original de monitoramento de chuva, que não havia sido explorada na literatura de forma quantitativa.

No description available.