A Nonstationary Negative Binomial Time Series with Time-Dependent Covariates: Enterococcus Counts in Boston Harbor

Boston Harbor has had a history of poor water quality, including contamination by enteric pathogens. We conduct a statistical analysis of data collected by the Massachusetts Water Resources Authority (MWRA) between 1996 and 2002 to evaluate the effects of court-mandated improvements in sewage treatment. Motivated by the ineffectiveness of standard Poisson mixture models and their zero-inflated counterparts, we propose a new negative binomial model for time series of Enterococcus counts in Boston Harbor, where nonstationarity and autocorrelation are modeled using a nonparametric smooth function of time in the predictor. Without further restrictions, this function is not identifiable in the presence of time-dependent covariates; consequently we use a basis orthogonal to the space spanned by the covariates and use penalized quasi-likelihood (PQL) for estimation. We conclude that Enterococcus counts were greatly reduced near the Nut Island Treatment Plant (NITP) outfalls following the transfer of wastewaters from NITP to the Deer Island Treatment Plant (DITP) and that the transfer of wastewaters from Boston Harbor to the offshore diffusers in Massachusetts Bay reduced the Enterococcus counts near the DITP outfalls.

Evaluation of flood risk in response to climate variability

Standard procedures for forecasting flood risk (Bulletin 17B) assume annual maximum flood (AMF) series are stationary, meaning the distribution of flood flows is not significantly affected by climatic trends/cycles, or anthropogenic activities within the watershed. Historical flood events are therefore considered representative of future flood occurrences, and the risk associated with a given flood magnitude is modeled as constant over time. However, in light of increasing evidence to the contrary, this assumption should be reconsidered, especially as the existence of nonstationarity in AMF series can have significant impacts on planning and management of water resources and relevant infrastructure. Research presented in this thesis quantifies the degree of nonstationarity evident in AMF series for unimpaired watersheds throughout the contiguous U.S., identifies meteorological, climatic, and anthropogenic causes of this nonstationarity, and proposes an extension of the Bulletin 17B methodology which yields forecasts of flood risk that reflect climatic influences on flood magnitude. To appropriately forecast flood risk, it is necessary to consider the driving causes of nonstationarity in AMF series. Herein, large-scale climate patterns—including El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO)—are identified as influencing factors on flood magnitude at numerous stations across the U.S. Strong relationships between flood magnitude and associated precipitation series were also observed for the majority of sites analyzed in the Upper Midwest and Northeastern regions of the U.S. Although relationships between flood magnitude and associated temperature series are not apparent, results do indicate that temperature is highly correlated with the timing of flood peaks. Despite consideration of watersheds classified as unimpaired, analyses also suggest that identified change-points in AMF series are due to dam construction, and other types of regulation and diversion. Although not explored herein, trends in AMF series are also likely to be partially explained by changes in land use and land cover over time. Results obtained herein suggest that improved forecasts of flood risk may be obtained using a simple modification of the Bulletin 17B framework, wherein the mean and standard deviation of the log-transformed flows are modeled as functions of climate indices associated with oceanic-atmospheric patterns (e.g. AMO, ENSO, NAO, and PDO) with lead times between 3 and 9 months. Herein, one-year ahead forecasts of the mean and standard deviation, and subsequently flood risk, are obtained by applying site specific multivariate regression models, which reflect the phase and intensity of a given climate pattern, as well as possible impacts of coupling of the climate cycles. These forecasts of flood risk are compared with forecasts derived using the existing Bulletin 17B model; large differences in the one-year ahead forecasts are observed in some locations. The increased knowledge of the inherent structure of AMF series and an improved understanding of physical and/or climatic causes of nonstationarity gained from this research should serve as insight for the formulation of a physical-casual based statistical model, incorporating both climatic variations and human impacts, for flood risk over longer planning horizons (e.g., 10-, 50, 100-years) necessary for water resources design, planning, and management.

Simulación de registros sísmicos a partir de espectros de diseño

La simulación de registros sísmicos, compatibles con espectros medios de diseño, se ha convertido en una necesidad en los últimos años, debido principalmente a su exigencia en la norma de regulación del cálculo de centrales nucleares. En este trabajo se presentan distintas posibilidades de realización de esta simulación, así como una comparación entre ellas, apareciendo como una alternativa efectiva a los métodos clásicos la utilización del contenido de fase de los seismos reales. Mediante ello se establece un procedimiento que elimina la arbitrariedad que supone el uso de una función envolvente para definir la característica no estacionaria del registro. Los distintos métodos son descritos detalladamente, así como la influencia de los diferentes parámetros que intervienen en cada uno de ellos. Por último se presentan algunos ejemplos numéricos = The simulation of spectrum-compatible earthquake time histories, has been a need since the beginning of the development of earthquake engineering for complicated structures. More than the safety of the main structure, the analysis of the equipment (piping, rackes, etc.) can be assesed only on the basis of time-histories of the floor in which they are contained. This paper presents several alternatives to the generation of sinthetic time histories and the use of the distribution of the differences among the phase-angles is demonstrated to be a useful tool to simulate the nonstationarity of the process. Thorugh the paper an extensive use is made of the F.F.T. algorithm.

(Table 1) Calculated sea surface temperature of sediment core RC11-210

Proxy indicators of sea surface temperature and equatorial divergence based on radiolarian assemblage data, and of trade wind intensity based on eolian grain size data show similar aspects of variability during the late Pleistocene: All indicators fluctuate at higher frequencies than the 100,000-year glacial-interglacial cycle, display reduced amplitude variations since 300,000 years ago, exhibit a change in the record character at about 300,000 years ago (the mid-Brunhes climatic event), and have higher amplitude variations in sediments 300,000-850,000 years old. Time series analyses were conducted to determine the spectral character of each record (delta18O of planktonic foraminifer, sea surface temperature values, equatorial divergence indicators, and wind intensity indicators) and to quantify interrecord coherence and phase relationships. The record was divided at the 300,000-year clear change in climatic variability (nonstationarity). The delta18O-based time scale is better lower in the core so our spectral analyses concentrated on the interval from 402,000-774,000 years. The delta18O spectra show 100,000- and 41,000-year power in the younger portion, 0-300,000 years, and 100,000-, 41,000- and 23,000-year power in the older interval, all highly coherent and in phase with the SPECMAP average stacked isotope record. Unlike the isotope record the dominant period in both the eolian grain size and equatorial divergence indicators is 31,000 years. This period is also important in the sea surface temperature signal where the dominant spectral peak is 100,000 years. The 31,000-year spectral component is coherent and in phase between the eolian and divergence records, confirming the link between atmospheric and ocean surface circulation for the first time in the paleoclimate record. Since the 31,000-year power appears in independent data sets within this core and also appears in other equatorial records [J. Imbrie personal communication, 1987], we assume it to be real and representative of both a nonlinear response to orbital forcing, possibly a combination of orbital tilt and eccentricity, and some resonance phenomenon required to amplify the response at this period so that it appears as a dominant frequency component. The mid-Brunhes climatic event is an important aspect of these records, but its cause remains unknown.

Nonstationary brand variables in category management: A cointegration perspective

Category-management models serve to assist in the development of plans for pricing and promotions of individual brands. Techniques to solve the models can have problems of accuracy and interpretability because they are susceptible to spurious regression problems due to nonstationary time-series data. Improperly stated nonstationary systems can reduce the accuracy of the forecasts and undermine the interpretation of the results. This is problematic because recent studies indicate that sales are often a nonstationary time-series. Newly developed correction techniques can account for nonstationarity by incorporating error-correction terms into the model when using a Bayesian Vector Error-Correction Model. The benefit of using such a technique is that shocks to control variates can be separated into permanent and temporary effects and allow cointegration of series for analysis purposes. Analysis of a brand data set indicates that this is important even at the brand level. Thus, additional information is generated that allows a decision maker to examine controllable variables in terms of whether they influence sales over a short or long duration. Only products that are nonstationary in sales volume can be manipulated for long-term profit gain, and promotions must be cointegrated with brand sales volume. The brand data set is used to explore the capabilities and interpretation of cointegration.

A time-domain test for some types of nonlinearity

The bispectrum and third-order moment can be viewed as equivalent tools for testing for the presence of nonlinearity in stationary time series. This is because the bispectrum is the Fourier transform of the third-order moment. An advantage of the bispectrum is that its estimator comprises terms that are asymptotically independent at distinct bifrequencies under the null hypothesis of linearity. An advantage of the third-order moment is that its values in any subset of joint lags can be used in the test, whereas when using the bispectrum the entire (or truncated) third-order moment is required to construct the Fourier transform. In this paper, we propose a test for nonlinearity based upon the estimated third-order moment. We use the phase scrambling bootstrap method to give a nonparametric estimate of the variance of our test statistic under the null hypothesis. Using a simulation study, we demonstrate that the test obtains its target significance level, with large power, when compared to an existing standard parametric test that uses the bispectrum. Further we show how the proposed test can be used to identify the source of nonlinearity due to interactions at specific frequencies. We also investigate implications for heuristic diagnosis of nonstationarity.

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We de. ne a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states i and j in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.

An Empirically Based Stochastic Turbulence Simulator with Temporal Coherence for Wind Energy Applications

In this dissertation, we develop a novel methodology for characterizing and simulating nonstationary, full-field, stochastic turbulent wind fields.

In this new method, nonstationarity is characterized and modeled via temporal coherence, which is quantified in the discrete frequency domain by probability distributions of the differences in phase between adjacent Fourier components.

The empirical distributions of the phase differences can also be extracted from measured data, and the resulting temporal coherence parameters can quantify the occurrence of nonstationarity in empirical wind data.

This dissertation (1) implements temporal coherence in a desktop turbulence simulator, (2) calibrates empirical temporal coherence models for four wind datasets, and (3) quantifies the increase in lifetime wind turbine loads caused by temporal coherence.

The four wind datasets were intentionally chosen from locations around the world so that they had significantly different ambient atmospheric conditions.

The prevalence of temporal coherence and its relationship to other standard wind parameters was modeled through empirical joint distributions (EJDs), which involved fitting marginal distributions and calculating correlations.

EJDs have the added benefit of being able to generate samples of wind parameters that reflect the characteristics of a particular site.

Lastly, to characterize the effect of temporal coherence on design loads, we created four models in the open-source wind turbine simulator FAST based on the \windpact turbines, fit response surfaces to them, and used the response surfaces to calculate lifetime turbine responses to wind fields simulated with and without temporal coherence.

The training data for the response surfaces was generated from exhaustive FAST simulations that were run on the high-performance computing (HPC) facilities at the National Renewable Energy Laboratory.

This process was repeated for wind field parameters drawn from the empirical distributions and for wind samples drawn using the recommended procedure in the wind turbine design standard \iec.

The effect of temporal coherence was calculated as a percent increase in the lifetime load over the base value with no temporal coherence.

Stakeholder-led science: engaging resource managers to identify science needs for long-term management of floodplain conservation lands

Floodplains pose challenges to managers of conservation lands because of constantly changing interactions with their rivers. Although scientific knowledge and understanding of the dynamics and drivers of river-floodplain systems can provide guidance to floodplain managers, the scientific process often occurs in isolation from management. Further, communication barriers between scientists and managers can be obstacles to appropriate application of scientific knowledge. With the coproduction of science in mind, our objectives were the following: (1) to document management priorities of floodplain conservation lands, and (2) identify science needs required to better manage the identified management priorities under nonstationary conditions, i.e., climate change, through stakeholder queries and interactions. We conducted an online survey with 80 resource managers of floodplain conservation lands along the Upper and Middle Mississippi River and Lower Missouri River, USA, to evaluate management priority, management intensity, and available scientific information for management objectives and conservation targets. Management objectives with the least information available relative to priority included controlling invasive species, maintaining respectful relationships with neighbors, and managing native, nongame species. Conservation targets with the least information available to manage relative to management priority included pollinators, marsh birds, reptiles, and shore birds. A follow-up workshop and survey focused on clarifying science needs to achieve management objectives under nonstationary conditions. Managers agreed that metrics of inundation, including depth and extent of inundation, and frequency, duration, and timing of inundation would be the most useful metrics for management of floodplain conservation lands with multiple objectives. This assessment provides guidance for developing relevant and accessible science products to inform management of highly dynamic floodplain environments. Although the problems facing managers of these lands are complex, products focused on a small suite of inundation metrics were determined to be the most useful to guide the decision making process.

Global interactions between fire and vegetation, human activities and climate

Doutoramento em Engenharia Florestal - Instituto Superior de Agronomia - UL

FLOOD RISK ASSESSMENT UNDER HISTORICAL AND PREDICTED LAND USE CHANGE USING CONTINUOUS HYDROLOGIC MODELING

Current procedures for flood risk estimation assume flood distributions are stationary over time, meaning annual maximum flood (AMF) series are not affected by climatic variation, land use/land cover (LULC) change, or management practices. Thus, changes in LULC and climate are generally not accounted for in policy and design related to flood risk/control, and historical flood events are deemed representative of future flood risk. These assumptions need to be re-evaluated, however, as climate change and anthropogenic activities have been observed to have large impacts on flood risk in many areas. In particular, understanding the effects of LULC change is essential to the study and understanding of global environmental change and the consequent hydrologic responses. The research presented herein provides possible causation for observed nonstationarity in AMF series with respect to changes in LULC, as well as a means to assess the degree to which future LULC change will impact flood risk. Four watersheds in the Midwest, Northeastern, and Central United States were studied to determine flood risk associated with historical and future projected LULC change. Historical single framed aerial images dating back to the mid-1950s were used along with Geographic Information Systems (GIS) and remote sensing models (SPRING and ERDAS) to create historical land use maps. The Forecasting Scenarios of Future Land Use Change (FORE-SCE) model was applied to generate future LULC maps annually from 2006 to 2100 for the conterminous U.S. based on the four IPCC-SRES future emission scenario conditions. These land use maps were input into previously calibrated Soil and Water Assessment Tool (SWAT) models for two case study watersheds. In order to isolate effects of LULC change, the only variable parameter was the Runoff Curve Number associated with the land use layer. All simulations were run with daily climate data from 1978-1999, consistent with the 'base' model which employed the 1992 NLCD to represent 'current' conditions. Output daily maximum flows were converted to instantaneous AMF series and were subsequently modeled using a Log-Pearson Type 3 (LP3) distribution to evaluate flood risk. Analysis of the progression of LULC change over the historic period and associated SWAT outputs revealed that AMF magnitudes tend to increase over time in response to increasing degrees of urbanization. This is consistent with positive trends in the AMF series identified in previous studies, although there are difficulties identifying correlations between LULC change and identified change points due to large time gaps in the generated historical LULC maps, mainly caused by unavailability of sufficient quality historic aerial imagery. Similarly, increases in the mean and median AMF magnitude were observed in response to future LULC change projections, with the tails of the distributions remaining reasonably constant. FORE-SCE scenario A2 was found to have the most dramatic impact on AMF series, consistent with more extreme projections of population growth, demands for growing energy sources, agricultural land, and urban expansion, while AMF outputs based on scenario B2 showed little changes for the future as the focus is on environmental conservation and regional solutions to environmental issues.