In numerous regions around the globe, climate change can be expected to change the pattern of severe weather events. Migration flows have been systematically larger the higher the proportion of the population in urban areas in the destination county relative to the origin county. Richer models demonstrate that the effects of a number of different types of extreme weather events (i.e. flooding, heat waves, and wildfires) in the origin county on county-to-county migration flows are statistically significantly greater when the destination county is more urbanized. The effect of the number of fatalities from flooding and heat waves in the origin county on migration flows is also amplified when the destination county is more urbanized. Thus it appears that even in a developed country like the U.S. extreme weather events still exacerbate rural-to-urban migration flows.

Made available in DSpace on 2014-06-11T19:32:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2007Bitstream added on 2014-06-13T19:25:54Z : No. of bitstreams: 1 berezuck_ag_dr_prud.pdf: 3585867 bytes, checksum: 369d960412156cb7787dd35cbfac2fd4 (MD5) / A análise dos modos de atuação dos mecanismos atmosféricos é uma das bases da ciência climatológica, pois, através dessas, os pesquisadores estão aptos a fazer as diagnoses regionais, as prognoses climáticas, avaliar o possível impacto de variações climáticas e desenvolver projetos para mitigá-los. Seguindo essa lógica, foi elaborada a análise de adversidade climática do oeste paulista e do norte do Paraná. Para a efetuação dessa tarefa, foi utilizada a técnica de análise rítmica de Monteiro, sendo pesquisados os anos de 1997, 1998 e 2001 nas localidades de Presidente Prudente, Maringá e Londrina. Além da pesquisa dos dados diários desses três anos, em que foram criados 108 gráficos de análise rítmica e 108 gráficos de eventos climáticos, foram analisados 27 anos de dados mensais e anuais (1976- 2003) que possibilitaram a elaboração de 54 figuras, 67 tabelas, 102 gráficos e 9 organogramas, em que foram analisadas as características atmosféricas regionais, observando possíveis tendências de alterações climáticas futuras. Foram constatados, ao longo da análise, que a área de estudo está passando por um processo de aquecimento de até 1ºC, além de um processo de fortalecimento sazonal, com as estações mais secas e chuvosas tornando-se mais bem definidas, o que pode causar, futuramente, uma maior quantidade de eventos extremos e adversidades climáticas, que, por sua vez, podem afetar as cidades e a produção agrícola. / Analyzing how the atmospheric mechanisms act is a great base of the climate science because it allows the researches to know about the regional weather aspects, to discuss the possible impact of climatic variations and to develop projects in order to protect the region against these possible variations in the soon future. Following this idea, we elaborated an analysis about hazards in the Western Sao Paolo State and the North Parana State in Brazil. We based on the Monteiro rhythm analysis method to study the cities Presidente Prudente, Maringa and Londrina in the years 1997, 1998 and 2001. Through the search of diary weather statistics we created 108 rhythm analysis graphics and 108 climatic event graphics. Besides them, we studied 27 years of mensal and annual statistics (1976 - 2003) which allowed the creation of 54 figures, 67 tables, 102 graphics and 9 examples. Through these we analyzed regional climatic aspects, searching for future tendencies of climate variations. It was verified by the analysis a warming of one Celsius degree at the studied areas and also a process of sazonalization which showed more clearly the division of wet and dry seasons. We see that it could result in the future more cases of hazards that could affect the cities and the agricultural production. The thesis revels that the climatic rhythms study, the interpretation of regional climatic variation and the study of the necessary actions to protect the areas against hazards are complex questions which involve great series of variables that, sometimes, the statistical techniques are not able to solve, despite its powerful and recognition in science. Because of that, it is extremely important the careful interpretation of climatic aspects by rhythm paradigm, mainly linking it with statistical techniques as well.

Yes / This book is an edition of the Special Issue Advances in Modelling and Prediction on the Impact of Human Activities and Extreme Events on Environments that was published in Water journal.

Bridge scour is the leading cause of bridge damage nationwide. Successfully mitigating bridge scour problems depends on our ability to reliably estimate scour potential, design safe and economical foundation elements that account for scour potential, identify vulnerabilities related to extreme events, and recognize changes to the environmental setting that increase risk at existing bridges. This study leverages available information, gathered from several statewide resources, and adds watershed metrics to create a comprehensive, georeferenced dataset to identify parameters that correlate to bridges damaged in an extreme flood event. Understanding the underlying relationships between existing bridge condition, fluvial stresses, and geomorphological changes is key to identifying vulnerabilities in both existing and future bridge infrastructure. In creating this comprehensive database of bridge inspection records and associated damage characterization, features were identified that correlate to and discriminate between levels of bridge damage. Stream geomorphic assessment features were spatially joined to every bridge, marking the first time that geomorphic assessments have been broadly used for estimating bridge vulnerability. Stream power assessments and watershed delineations for every bridge and stream reach were generated to supplement the comprehensive database. Individual features were tested for their significance to discriminate bridge damage, and then used to create empirical fragility curves and probabilistic predictions maps to aid in future bridge vulnerability detection. Damage to over 300 Vermont bridges from a single extreme flood event, the August 28, 2011 Tropical Storm Irene, was used as the basis for this study. Damage to historic bridges was also summarized and tabulated. In some areas of Vermont, the storm rainfall recurrence interval exceeded 500 years, causing widespread flooding and damaging over 300 bridges. With a dataset of over 330 features for more than 2,000 observations to bridges that were damaged as well as not damaged in the storm, an advanced evolutionary algorithm performed multivariate feature selection to overcome the shortfalls of traditional logistic regression analysis. The analysis identified distinct combinations of variables that correlate to the observed bridge damage under extreme food events.

The thesis describes the methodology of evaluation of extreme events and development of cartographic information system for this purpose. Existing complex risk assessment systems in the world are analysed highlighting their advantages and disadvantages. Author proposes original integrated risk assessment methodology based on integration of information from different geographic data sources. A cartographic information system designed by the author allows for the assessment of extreme events threats and risks. The developed methodology includes methodology of cartographic information system component development and deployment. The work describes necessary extreme events data, methods of their collection and database design principles. The created model enables the user to collect the data on extreme hazard events and to aggregate several threats into a single synthetic threat. The concepts of risks and threats and risk assessment methodology are explained. The author introduces project of an information system operating in the Lithuanian Geographic Information Infrastructure and integrated in the Lithuania spatial information portal. The system is tested with several consistent spatial data sets for Lithuania. The thesis presents experimental results that show increased geological and meteorological risk areas in Lithuania. Finally, methodological and practical conclusions about the methods and system customization, reliability and compliance with standards are presented. / Disertacijoje aprašoma ekstremalių įvykių vertinimo kartografinės informacinės sistemos kūrimo metodologija. Analizuojamos pasaulyje egzistuojančios kompleksinės rizikos vertinimo sistemos išryškinami jų trūkumai ir privalumai. Atliktos analizės pagrindu sukuriama originali daugeliu duomenų šaltinių pagrįsta kompleksinio rizikos vertinimo metodologija ir aprašoma autoriaus suprojektuota informacinė sistema leidžianti vertinti ekstremalių įvykių grėsmes ir riziką. Sukurta metodologija apima kartografinės informacinės sistemos sudedamųjų dalių kūrimo ir diegimo metodiką. Aprašomi sistemos veikimui reikiamų duomenų tipai, jų surinkimas, ekstremalių įvykių duomenų bazės kaupimo principai, sukuriamas ekstremalių įvykių grėsmių skaičiavimo ir kelių grėsmių apjungimo į vieną sintetinę grėsmę modelis. Aprašomas rizikos ir grėsmės santykis ir rizikos vertinimo metodologija. Disertacijoje taip pat pateikiama visos sistemos, veikiančios Lietuvos geografinės informacijos infrastruktūroje, ir integruotos Lietuvos erdvinės informacijos portale projektas. Sistema išbandyta su Lietuvoje pasiekiamais ir realiai egzistuojančiais erdvinių duomenų rinkiniais. Pateikiami eksperimento metu gauti rezultatai, rodantys padidintų geologinių ir meteorologinių rizikos rajonus Lietuvoje. Darbo pabaigoje pateikiamos metodologinės ir praktinės išvados apie metodų ir sistemos pritaikymą, patikimumą ir atitikimą standartams.

One major acknowledged challenge in daily precipitation is the inability to model extreme events in the spectrum of events. These extreme events are rare but may cause large losses. How to realistically simulate extreme behavior of daily precipitation is necessary and important. To that end, a hybrid probability distribution is developed. The logic of this distribution is to simulate the low to moderate values by an exponential distribution and extremes by a generalized Pareto distribution. Compared with alternatives, the developed hybrid distribution is capable of simulating the entire range of precipitation amount and is much easier to use. The hybrid distribution is then used to construct a bivariate discrete-continuous mixed distribution, which is used for building a daily precipitation generator. The developed generator can successfully reproduce extreme events. Compared with other widely used generators, the most important advantage of the developed generator is that it is apt at extrapolating values significantly beyond the upper range of observed data. The major challenge in monthly streamflow simulation is referred to the underrepresentation of inter-annual variability. The inter-annual variability is often related with sustained droughts or periods of high flows. Preserving inter-annual variability is thus of particular importance for the long-term management of water resources systems. To that end, variables conveying such inter-annual signals should be used as covariates. This requires models that must be flexible at incorporating as many covariates as necessary. Keeping this point in mind, a joint conditional density estimation network is developed. Therein, the joint distribution of streamflows of two adjacent months is assumed to follow a specific parametric family. Parameters of the distribution are estimated by an artificial neural network. Due to the seasonal concentration of precipitation or the joint effect of rainfall and snowmelt, monthly streamflow distribution sometimes may exhibit a bimodal shape. To reproduce bimodality, nonparametric models are often preferred. However, the simulated sequences from existing nonparametric models represent too close a resemblance to historical record. To address this issue, while retaining typical merits of nonparametric models, a multi-model regression-sampling algorithm with a few weak assumptions is developed. Collecting hydrometric data is the first step for building hydrologic models, and for planning, design, operation, and management of water resource systems. In this dissertation, an entropy-theory-based criterion, termed maximum information minimum redundancy, is proposed for hydrometric monitoring network evaluation and design. Compared with existing similar approaches, the criterion is apt at finding stations with high information content, and locating independent stations.

Orientador: João Lima Sant'Anna Neto / Banca: Margarete Cristiane de Costa Trindade Amorim / Banca: José Tadeu Garcia Tommaselli / Banca: Inês Moresco Danni-Oliveira / Banca: Emerson Galvani / A análise dos modos de atuação dos mecanismos atmosféricos é uma das bases da ciência climatológica, pois, através dessas, os pesquisadores estão aptos a fazer as diagnoses regionais, as prognoses climáticas, avaliar o possível impacto de variações climáticas e desenvolver projetos para mitigá-los. Seguindo essa lógica, foi elaborada a análise de adversidade climática do oeste paulista e do norte do Paraná. Para a efetuação dessa tarefa, foi utilizada a técnica de análise rítmica de Monteiro, sendo pesquisados os anos de 1997, 1998 e 2001 nas localidades de Presidente Prudente, Maringá e Londrina. Além da pesquisa dos dados diários desses três anos, em que foram criados 108 gráficos de análise rítmica e 108 gráficos de eventos climáticos, foram analisados 27 anos de dados mensais e anuais (1976- 2003) que possibilitaram a elaboração de 54 figuras, 67 tabelas, 102 gráficos e 9 organogramas, em que foram analisadas as características atmosféricas regionais, observando possíveis tendências de alterações climáticas futuras. Foram constatados, ao longo da análise, que a área de estudo está passando por um processo de aquecimento de até 1ºC, além de um processo de fortalecimento sazonal, com as estações mais secas e chuvosas tornando-se mais bem definidas, o que pode causar, futuramente, uma maior quantidade de eventos extremos e adversidades climáticas, que, por sua vez, podem afetar as cidades e a produção agrícola. / Analyzing how the atmospheric mechanisms act is a great base of the climate science because it allows the researches to know about the regional weather aspects, to discuss the possible impact of climatic variations and to develop projects in order to protect the region against these possible variations in the soon future. Following this idea, we elaborated an analysis about hazards in the Western Sao Paolo State and the North Parana State in Brazil. We based on the Monteiro rhythm analysis method to study the cities Presidente Prudente, Maringa and Londrina in the years 1997, 1998 and 2001. Through the search of diary weather statistics we created 108 rhythm analysis graphics and 108 climatic event graphics. Besides them, we studied 27 years of mensal and annual statistics (1976 - 2003) which allowed the creation of 54 figures, 67 tables, 102 graphics and 9 examples. Through these we analyzed regional climatic aspects, searching for future tendencies of climate variations. It was verified by the analysis a warming of one Celsius degree at the studied areas and also a process of sazonalization which showed more clearly the division of wet and dry seasons. We see that it could result in the future more cases of hazards that could affect the cities and the agricultural production. The thesis revels that the climatic rhythms study, the interpretation of regional climatic variation and the study of the necessary actions to protect the areas against hazards are complex questions which involve great series of variables that, sometimes, the statistical techniques are not able to solve, despite its powerful and recognition in science. Because of that, it is extremely important the careful interpretation of climatic aspects by rhythm paradigm, mainly linking it with statistical techniques as well. / Doutor

Climate change studies are crucial to assist decision-makers in understanding future risks and planning adequate adaptation measures. In general, Global/Regional Climate Models (GCMs/RCMs) achieve coarse resolutions, and are thus unable to provide sufficient information to conduct local climate assessments. Downscaling, defined as a method that derives local to regional-scale (10 to 100 km) information from larger-scale models or data analyses, is used to address this deficiency. In this thesis, a particular downscaling technique, known as the Quantile-Quantile transformation, was used to adjust the statistical distribution of RCM variables to match the statistical distribution of the observed variables generated by two RCMs: the Canadian Regional Climate Model version 3.7.1 and the ARPEGE model, on the historical period (1961-2001). The analyses presented in this study were applied to daily precipitation and maximum and minimum temperatures in the South Nation watershed in Eastern Ontario, Canada. The two-sample Kolmogorov–Smirnov test indicated that the Quantile-Quantile transformation improved the shape of the PDF of RCM-simulated climate variables. The results suggest that, under the A1B scenario, temperatures in the watershed would rise significantly and there would be an increment in precipitation occurrence and intensity. Trend analysis was performed on the 1961 to 2001 and 2041 to 2081 timeframes, using the Mann-Kendall test and the Sen's slope estimator. Discernible, often significant, increases of maximum and minimum temperatures were found for the 1961 to 2001 period, and stronger ascending slopes for the 2041 to 2081 period. However, there was marginal evidence of changes in the time series of maximum and accumulated annual precipitation for both periods. The study also outlined how the frequency and intensity of some extreme weather events will evolve in the 2041-2081 period in response to the rise in atmospheric GHG concentrations. Projected impacts were investigated by tracking future changes in four extreme temperature indices and three precipitation indices. It was predicted that heavy precipitation events and warm spells will occur more frequently and intensely, while extreme cold events will be weaker, and some will be hardly observed.

The Sendai Framework for Disaster Risk Reduction calls for collaboration across sectors in society as an effective way to reduce disaster risk in order to safeguard lives, human wellbeing, and development gains from potential disasters. However, the effectiveness of these cross-sector collaboration approaches has most often been studied in the context of rapid onset disasters such as floods, with less focus on slow-onset disasters such as multiple year droughts. There is also limited research on the contributions of cross-sector collaborations towards Disaster Risk Reduction in African cities. For this study, I set out to investigate cross-sector collaboration efforts contributing to drought response in the city of Harare, Zimbabwe; and how these collaborations were contributing towards fulfilling the four priority areas of the Sendai Framework. These include i) understanding disaster risk, ii) strengthening disaster risk governance, iii) investing in disaster risk reduction for resilience, and iv) enhancing disaster preparedness for effective response. In this qualitative study, I made use of purposive and snowball sampling methods to select 14 key informants from national and local government, non-governmental organisations, and civil society organisations who were directly involved in eight drought related cross-sector collaborations. I conducted in depth semi-structured interviews with these key informants between 2018 and 2019. For the analysis, I followed prior developed themes based on Bryson et al. (2006) theoretical framework to understand cross-sector collaborations. I also applied a typology of barriers and enablers developed from the literature in the analysis, and interpreted emergent themes using NVivo software. I then assessed the contributions of the identified cross-sector collaborations to the activities listed under each of the four priority areas in the Sendai Framework. The findings highlighted the socio-political and economic context of the city of Harare had seeped through and influenced the cross-sector collaborations responding to drought. They shed light on how taking advantage of widely accessible social media platforms serve to enhance collaborations. Additionally, the results highlight the importance of existing networks and relationships in enabling cross-sector collaborations to occur effectively. Most of the cross-sector collaborations occurring in response to drought were originally formed for other reasons and included drought response during the peak of the drought disaster. Collaborations were effective at contributing towards engaging communities in risk assessment and reporting at the local level (Priority 1) and ensuring continual provisioning of services (water) during and after disasters (Priority 4). I make suggestions for collaborations to consider issues of power and how these affect the effectiveness of collaborations on the ground with regards to ensuring social justice and reducing inequality. Finally, I conclude that cross-sector collaborations would be more effective in response to slow-onset disasters when they are formed and applied before the disaster is at its peak. I also suggest that the design and implementation of the cross-sector collaborations be tailor made to consider the socio-political and economic aspects of the city in their design for effective response.

Modeling extreme events is one of the central tasks in risk management and planning, as catastrophes and crises put human lives and financial assets at stake. A common approach to estimate the likelihood of extreme events, using extreme value theory (EVT), studies the asymptotic behavior of the ``tail" portion of data, and suggests suitable parametric distributions to fit the data backed up by their limiting behaviors as the data size or the excess threshold grows. We explore an alternate approach to estimate extreme events that is inspired from recent advances in robust optimization. Our approach represents information about tail behaviors as constraints and attempts to estimate a target extremal quantity of interest (e.g, tail probability above a given high level) by imposing an optimization problem to find a conservative estimate subject to the constraints that encode the tail information capturing belief on the tail distributional shape. We first study programs where the feasible region is restricted to distribution functions with convex tail densities, a feature shared by all common parametric tail distributions. We then extend our work by generalizing the feasible region to distribution functions with monotone derivatives and bounded or infinite moments. In both cases, we study the statistical implications of the resulting optimization problems. Through investigating their optimality structures, we also present how the worst-case tail in general behaves as a linear combination of polynomial decay tails. Numerically, we develop results to reduce these optimization problems into tractable forms that allow solution schemes via linear-programming-based techniques.