Climate variability effects on field crops in Iberian Peninsula. Predictability

La presente Tesis constituye un avance en el conocimiento de los efectos de la variabilidad climática en los cultivos en la Península Ibérica (PI). Es bien conocido que la temperatura del océano, particularmente de la región tropical, es una de las variables más convenientes para ser utilizado como predictor climático. Los océanos son considerados como la principal fuente de almacenamiento de calor del planeta debido a la alta capacidad calorífica del agua. Cuando se libera esta energía, altera los regímenes globales de circulación atmosférica por mecanismos de teleconexión. Estos cambios en la circulación general de la atmósfera afectan a la temperatura, precipitación, humedad, viento, etc., a escala regional, los cuales afectan al crecimiento, desarrollo y rendimiento de los cultivos. Para el caso de Europa, esto implica que la variabilidad atmosférica en una región específica se asocia con la variabilidad de otras regiones adyacentes y/o remotas, como consecuencia Europa está siendo afectada por los patrones de circulaciones globales, que a su vez, se ven afectados por patrones oceánicos. El objetivo general de esta tesis es analizar la variabilidad del rendimiento de los cultivos y su relación con la variabilidad climática y teleconexiones, así como evaluar su predictibilidad. Además, esta Tesis tiene como objetivo establecer una metodología para estudiar la predictibilidad de las anomalías del rendimiento de los cultivos. El análisis se centra en trigo y maíz como referencia para otros cultivos de la PI, cultivos de invierno en secano y cultivos de verano en regadío respectivamente. Experimentos de simulación de cultivos utilizando una metodología en cadena de modelos (clima + cultivos) son diseñados para evaluar los impactos de los patrones de variabilidad climática en el rendimiento y su predictibilidad. La presente Tesis se estructura en dos partes: La primera se centra en el análisis de la variabilidad del clima y la segunda es una aplicación de predicción cuantitativa de cosechas. La primera parte está dividida en 3 capítulos y la segundo en un capitulo cubriendo los objetivos específicos del presente trabajo de investigación. Parte I. Análisis de variabilidad climática El primer capítulo muestra un análisis de la variabilidad del rendimiento potencial en una localidad como indicador bioclimático de las teleconexiones de El Niño con Europa, mostrando su importancia en la mejora de predictibilidad tanto en clima como en agricultura. Además, se presenta la metodología elegida para relacionar el rendimiento con las variables atmosféricas y oceánicas. El rendimiento de los cultivos es parcialmente determinado por la variabilidad climática atmosférica, que a su vez depende de los cambios en la temperatura de la superficie del mar (TSM). El Niño es el principal modo de variabilidad interanual de la TSM, y sus efectos se extienden en todo el mundo. Sin embargo, la predictibilidad de estos impactos es controversial, especialmente aquellos asociados con la variabilidad climática Europea, que se ha encontrado que es no estacionaria y no lineal. Este estudio mostró cómo el rendimiento potencial de los cultivos obtenidos a partir de datos de reanálisis y modelos de cultivos sirve como un índice alternativo y más eficaz de las teleconexiones de El Niño, ya que integra las no linealidades entre las variables climáticas en una única serie temporal. Las relaciones entre El Niño y las anomalías de rendimiento de los cultivos son más significativas que las contribuciones individuales de cada una de las variables atmosféricas utilizadas como entrada en el modelo de cultivo. Además, la no estacionariedad entre El Niño y la variabilidad climática europea se detectan con mayor claridad cuando se analiza la variabilidad de los rendimiento de los cultivos. La comprensión de esta relación permite una cierta predictibilidad hasta un año antes de la cosecha del cultivo. Esta predictibilidad no es constante, sino que depende tanto la modulación de la alta y baja frecuencia. En el segundo capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de verano en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de maíz en la PI para todo el siglo veinte, usando un modelo de cultivo calibrado en 5 localidades españolas y datos climáticos de reanálisis para obtener series temporales largas de rendimiento potencial. Este estudio evalúa el uso de datos de reanálisis para obtener series de rendimiento de cultivos que dependen solo del clima, y utilizar estos rendimientos para analizar la influencia de los patrones oceánicos y atmosféricos. Los resultados muestran una gran fiabilidad de los datos de reanálisis. La distribución espacial asociada a la primera componente principal de la variabilidad del rendimiento muestra un comportamiento similar en todos los lugares estudiados de la PI. Se observa una alta correlación lineal entre el índice de El Niño y el rendimiento, pero no es estacionaria en el tiempo. Sin embargo, la relación entre la temperatura del aire y el rendimiento se mantiene constante a lo largo del tiempo, siendo los meses de mayor influencia durante el período de llenado del grano. En cuanto a los patrones atmosféricos, el patrón Escandinavia presentó una influencia significativa en el rendimiento en PI. En el tercer capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de invierno en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de trigo en secano del Noreste (NE) de la PI. La variabilidad climática es el principal motor de los cambios en el crecimiento, desarrollo y rendimiento de los cultivos, especialmente en los sistemas de producción en secano. En la PI, los rendimientos de trigo son fuertemente dependientes de la cantidad de precipitación estacional y la distribución temporal de las mismas durante el periodo de crecimiento del cultivo. La principal fuente de variabilidad interanual de la precipitación en la PI es la Oscilación del Atlántico Norte (NAO), que se ha relacionado, en parte, con los cambios en la temperatura de la superficie del mar en el Pacífico Tropical (El Niño) y el Atlántico Tropical (TNA). La existencia de cierta predictibilidad nos ha animado a analizar la posible predicción de los rendimientos de trigo en la PI utilizando anomalías de TSM como predictor. Para ello, se ha utilizado un modelo de cultivo (calibrado en dos localidades del NE de la PI) y datos climáticos de reanálisis para obtener series temporales largas de rendimiento de trigo alcanzable y relacionar su variabilidad con anomalías de la TSM. Los resultados muestran que El Niño y la TNA influyen en el desarrollo y rendimiento del trigo en el NE de la PI, y estos impactos depende del estado concurrente de la NAO. Aunque la relación cultivo-TSM no es igual durante todo el periodo analizado, se puede explicar por un mecanismo eco-fisiológico estacionario. Durante la segunda mitad del siglo veinte, el calentamiento (enfriamiento) en la superficie del Atlántico tropical se asocia a una fase negativa (positiva) de la NAO, que ejerce una influencia positiva (negativa) en la temperatura mínima y precipitación durante el invierno y, por lo tanto, aumenta (disminuye) el rendimiento de trigo en la PI. En relación con El Niño, la correlación más alta se observó en el período 1981 -2001. En estas décadas, los altos (bajos) rendimientos se asocian con una transición El Niño - La Niña (La Niña - El Niño) o con eventos de El Niño (La Niña) que están finalizando. Para estos eventos, el patrón atmosférica asociada se asemeja a la NAO, que también influye directamente en la temperatura máxima y precipitación experimentadas por el cultivo durante la floración y llenado de grano. Los co- efectos de los dos patrones de teleconexión oceánicos ayudan a aumentar (disminuir) la precipitación y a disminuir (aumentar) la temperatura máxima en PI, por lo tanto el rendimiento de trigo aumenta (disminuye). Parte II. Predicción de cultivos. En el último capítulo se analiza los beneficios potenciales del uso de predicciones climáticas estacionales (por ejemplo de precipitación) en las predicciones de rendimientos de trigo y maíz, y explora métodos para aplicar dichos pronósticos climáticos en modelos de cultivo. Las predicciones climáticas estacionales tienen un gran potencial en las predicciones de cultivos, contribuyendo de esta manera a una mayor eficiencia de la gestión agrícola, seguridad alimentaria y de subsistencia. Los pronósticos climáticos se expresan en diferentes formas, sin embargo todos ellos son probabilísticos. Para ello, se evalúan y aplican dos métodos para desagregar las predicciones climáticas estacionales en datos diarios: 1) un generador climático estocástico condicionado (predictWTD) y 2) un simple re-muestreador basado en las probabilidades del pronóstico (FResampler1). Los dos métodos se evaluaron en un caso de estudio en el que se analizaron los impactos de tres escenarios de predicciones de precipitación estacional (predicción seco, medio y lluvioso) en el rendimiento de trigo en secano, sobre las necesidades de riego y rendimiento de maíz en la PI. Además, se estimó el margen bruto y los riesgos de la producción asociada con las predicciones de precipitación estacional extremas (seca y lluviosa). Los métodos predWTD y FResampler1 usados para desagregar los pronósticos de precipitación estacional en datos diarios, que serán usados como inputs en los modelos de cultivos, proporcionan una predicción comparable. Por lo tanto, ambos métodos parecen opciones factibles/viables para la vinculación de los pronósticos estacionales con modelos de simulación de cultivos para establecer predicciones de rendimiento o las necesidades de riego en el caso de maíz. El análisis del impacto en el margen bruto de los precios del grano de los dos cultivos (trigo y maíz) y el coste de riego (maíz) sugieren que la combinación de los precios de mercado previstos y la predicción climática estacional pueden ser una buena herramienta en la toma de decisiones de los agricultores, especialmente en predicciones secas y/o localidades con baja precipitación anual. Estos métodos permiten cuantificar los beneficios y riesgos de los agricultores ante una predicción climática estacional en la PI. Por lo tanto, seríamos capaces de establecer sistemas de alerta temprana y diseñar estrategias de adaptación del manejo del cultivo para aprovechar las condiciones favorables o reducir los efectos de condiciones adversas. La utilidad potencial de esta Tesis es la aplicación de las relaciones encontradas para predicción de cosechas de la próxima campaña agrícola. Una correcta predicción de los rendimientos podría ayudar a los agricultores a planear con antelación sus prácticas agronómicas y todos los demás aspectos relacionados con el manejo de los cultivos. Esta metodología se puede utilizar también para la predicción de las tendencias futuras de la variabilidad del rendimiento en la PI. Tanto los sectores públicos (mejora de la planificación agrícola) como privados (agricultores, compañías de seguros agrarios) pueden beneficiarse de esta mejora en la predicción de cosechas. ABSTRACT The present thesis constitutes a step forward in advancing of knowledge of the effects of climate variability on crops in the Iberian Peninsula (IP). It is well known that ocean temperature, particularly the tropical ocean, is one of the most convenient variables to be used as climate predictor. Oceans are considered as the principal heat storage of the planet due to the high heat capacity of water. When this energy is released, it alters the global atmospheric circulation regimes by teleconnection1 mechanisms. These changes in the general circulation of the atmosphere affect the regional temperature, precipitation, moisture, wind, etc., and those influence crop growth, development and yield. For the case of Europe, this implies that the atmospheric variability in a specific region is associated with the variability of others adjacent and/or remote regions as a consequence of Europe being affected by global circulations patterns which, in turn, are affected by oceanic patterns. The general objective of this Thesis is to analyze the variability of crop yields at climate time scales and its relation to the climate variability and teleconnections, as well as to evaluate their predictability. Moreover, this Thesis aims to establish a methodology to study the predictability of crop yield anomalies. The analysis focuses on wheat and maize as a reference crops for other field crops in the IP, for winter rainfed crops and summer irrigated crops respectively. Crop simulation experiments using a model chain methodology (climate + crop) are designed to evaluate the impacts of climate variability patterns on yield and its predictability. The present Thesis is structured in two parts. The first part is focused on the climate variability analyses, and the second part is an application of the quantitative crop forecasting for years that fulfill specific conditions identified in the first part. This Thesis is divided into 4 chapters, covering the specific objectives of the present research work. Part I. Climate variability analyses The first chapter shows an analysis of potential yield variability in one location, as a bioclimatic indicator of the El Niño teleconnections with Europe, putting forward its importance for improving predictability in both climate and agriculture. It also presents the chosen methodology to relate yield with atmospheric and oceanic variables. Crop yield is partially determined by atmospheric climate variability, which in turn depends on changes in the sea surface temperature (SST). El Niño is the leading mode of SST interannual variability, and its impacts extend worldwide. Nevertheless, the predictability of these impacts is controversial, especially those associated with European climate variability, which have been found to be non-stationary and non-linear. The study showed how potential2 crop yield obtained from reanalysis data and crop models serves as an alternative and more effective index of El Niño teleconnections because it integrates the nonlinearities between the climate variables in a unique time series. The relationships between El Niño and crop yield anomalies are more significant than the individual contributions of each of the atmospheric variables used as input in the crop model. Additionally, the non-stationarities between El Niño and European climate variability are more clearly detected when analyzing crop-yield variability. The understanding of this relationship allows for some predictability up to one year before the crop is harvested. This predictability is not constant, but depends on both high and low frequency modulation. The second chapter identifies the oceanic and atmospheric patterns of climate variability affecting summer cropping systems in the IP. Moreover, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of maize yield variability in IP for the whole twenty century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate dependent time series of simulated crop yield for the whole region, and to use these yield to analyze the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non-stationary on time, although the air temperature-yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The third chapter identifies the oceanic and atmospheric patterns of climate variability affecting winter cropping systems in the IP. Also, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of rainfed wheat yield variability in IP. Climate variability is the main driver of changes in crop growth, development and yield, especially for rainfed production systems. In IP, wheat yields are strongly dependent on seasonal rainfall amount and temporal distribution of rainfall during the growing season. The major source of precipitation interannual variability in IP is the North Atlantic Oscillation (NAO) which has been related in part with changes in the Tropical Pacific (El Niño) and Atlantic (TNA) sea surface temperature (SST). The existence of some predictability has encouraged us to analyze the possible predictability of the wheat yield in the IP using SSTs anomalies as predictor. For this purpose, a crop model with a site specific calibration for the Northeast of IP and reanalysis climate datasets have been used to obtain long time series of attainable wheat yield and relate their variability with SST anomalies. The results show that El Niño and TNA influence rainfed wheat development and yield in IP and these impacts depend on the concurrent state of the NAO. Although crop-SST relationships do not equally hold on during the whole analyzed period, they can be explained by an understood and stationary ecophysiological mechanism. During the second half of the twenty century, the positive (negative) TNA index is associated to a negative (positive) phase of NAO, which exerts a positive (negative) influence on minimum temperatures (Tmin) and precipitation (Prec) during winter and, thus, yield increases (decreases) in IP. In relation to El Niño, the highest correlation takes place in the period 1981-2001. For these decades, high (low) yields are associated with an El Niño to La Niña (La Niña to El Niño) transitions or to El Niño events finishing. For these events, the regional associated atmospheric pattern resembles the NAO, which also influences directly on the maximum temperatures (Tmax) and precipitation experienced by the crop during flowering and grain filling. The co-effects of the two teleconnection patterns help to increase (decrease) the rainfall and decrease (increase) Tmax in IP, thus on increase (decrease) wheat yield. Part II. Crop forecasting The last chapter analyses the potential benefits for wheat and maize yields prediction from using seasonal climate forecasts (precipitation), and explores methods to apply such a climate forecast to crop models. Seasonal climate prediction has significant potential to contribute to the efficiency of agricultural management, and to food and livelihood security. Climate forecasts come in different forms, but probabilistic. For this purpose, two methods were evaluated and applied for disaggregating seasonal climate forecast into daily weather realizations: 1) a conditioned stochastic weather generator (predictWTD) and 2) a simple forecast probability resampler (FResampler1). The two methods were evaluated in a case study where the impacts of three scenarios of seasonal rainfall forecasts on rainfed wheat yield, on irrigation requirements and yields of maize in IP were analyzed. In addition, we estimated the economic margins and production risks associated with extreme scenarios of seasonal rainfall forecasts (dry and wet). The predWTD and FResampler1 methods used for disaggregating seasonal rainfall forecast into daily data needed by the crop simulation models provided comparable predictability. Therefore both methods seem feasible options for linking seasonal forecasts with crop simulation models for establishing yield forecasts or irrigation water requirements. The analysis of the impact on gross margin of grain prices for both crops and maize irrigation costs suggests the combination of market prices expected and the seasonal climate forecast can be a good tool in farmer’s decision-making, especially on dry forecast and/or in locations with low annual precipitation. These methodologies would allow quantifying the benefits and risks of a seasonal weather forecast to farmers in IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. The potential usefulness of this Thesis is to apply the relationships found to crop forecasting on the next cropping season, suggesting opportunity time windows for the prediction. The methodology can be used as well for the prediction of future trends of IP yield variability. Both public (improvement of agricultural planning) and private (decision support to farmers, insurance companies) sectors may benefit from such an improvement of crop forecasting.

The future of the ABM treaty : hearing before the International Security, Proliferation, and Federal Services Subcommittee of the Committee on Governmental Affairs, United States Senate, One Hundred Sixth Congress, first session, April 28, 1999.

Shipping list no.: 2000-0165-P.

Disaster preparedness : publications, films, and other audio-visual materials from the National Weather Service.

Mode of access: Internet.

Notifying floodplain residents : assessment of public information programs /

"March 4, 1983"

Emergency Prevention and Situation Monitoring in Carpathian Region

Carpathian region is well known as tectonically active zone. So, in addition to common problems of such region, as common water floods, possible mudflows and landslides a local seismic activity must be taken in account. In this paper a main points of situation monitoring in Carpathian region and ways how they help in emergency prevention are described. A short overview of existing solutions and future approach is being made.

Disaster Risk Management Disparity in the Caribbean: Evidence from Barbados, Dominican Republic, Jamaica and Trinidad and Tobago

This study on risk and disaster management capacities of four Caribbean countries: Barbados, the Dominican Republic, Jamaica, and Trinidad and Tobago, examines three main dimensions: 1) the impact of natural disasters from 1900 to 2010 (number of events, number of people killed, total number affected, and damage in US$); 2) institutional assessments of disaster risk management disparity; and 3) the 2010 Inter-American Bank for Development (IADB) Disaster Risk and Risk Management indicators for the countries under study. The results show high consistency among the different sources examined, pointing out the need to extend the IADB measurements to the rest of the Caribbean countries. Indexes and indicators constitute a comparison measure vis-à-vis existing benchmarks in order to anticipate a capacity to deal with adverse events and their consequences; however, the indexes and indicators could only be tested against the occurrence of a real event. Therefore, the need exists to establish a sustainable and comprehensive evaluation system after important disasters to assess a country‘s performance, verify the indicators, and gain feedback on measurement systems and methodologies. There is diversity in emergency and preparedness for disasters in the four countries under study. The nature of the event (hurricanes, earthquakes, floods, and seismic activity), especially its frequency and the intensity of the damage experienced, is related to how each has designed its risk and disaster management policies and programs to face natural disasters. Vulnerabilities to disaster risks have been increasing, among other factors, because of uncontrolled urbanization, demographic density and poverty increase, social and economic marginalization, and lack of building code enforcement. The four countries under study have shown improvements in risk management capabilities, yet they are far from being completed prepared. Barbados‘ risk management performance is superior, in comparison, to the majority of the countries of the region. However, is still far in achieving high performance levels and sustainability in risk management, primarily when it has the highest gap between potential macroeconomic and financial losses and the ability to face them. The Dominican Republic has shown steady risk performance up to 2008, but two remaining areas for improvement are hazard monitoring and early warning systems. Jamaica has made uneven advances between 1990 and 2008, requiring significant improvements to achieve high performance levels and sustainability in risk management, as well as macroeconomic mitigation infrastructure. Trinidad and Tobago has the lowest risk management score of the 15 countries in the Latin American and Caribbean region as assessed by the IADB study in 2010, yet it has experienced an important vulnerability reduction. In sum, the results confirmed the high disaster risk management disparity in the Caribbean region.

Disater Risk Management Disparity in the Caribbean: Evidence from Barbados, Dominican Republic, Jamaica and Trinidad and Tobago

This study on risk and disaster management capacities of four Caribbean countries: Barbados, the Dominican Republic, Jamaica, and Trinidad and Tobago, examines three main dimensions: 1) the impact of natural disasters from 1900 to 2010 (number of events, number of people killed, total number affected, and damage in US$); 2) institutional assessments of disaster risk management disparity; and 3) the 2010 Inter-American Bank for Development (IADB) Disaster Risk and Risk Management indicators for the countries under study. The results show high consistency among the different sources examined, pointing out the need to extend the IADB measurements to the rest of the Caribbean countries. Indexes and indicators constitute a comparison measure vis-à-vis existing benchmarks in order to anticipate a capacity to deal with adverse events and their consequences; however, the indexes and indicators could only be tested against the occurrence of a real event. Therefore, the need exists to establish a sustainable and comprehensive evaluation system after important disasters to assess a country’s performance, verify the indicators, and gain feedback on measurement systems and methodologies. There is diversity in emergency and preparedness for disasters in the four countries under study. The nature of the event (hurricanes, earthquakes, floods, and seismic activity), especially its frequency and the intensity of the damage experienced, is related to how each has designed its risk and disaster management policies and programs to face natural disasters. Vulnerabilities to disaster risks have been increasing, among other factors, because of uncontrolled urbanization, demographic density and poverty increase, social and economic marginalization, and lack of building code enforcement. The four countries under study have shown improvements in risk management capabilities, yet they are far from being completed prepared. Barbados’ risk management performance is superior, in comparison, to the majority of the countries of the region. However, is still far in achieving high performance levels and sustainability in risk management, primarily when it has the highest gap between potential macroeconomic and financial losses and the ability to face them. The Dominican Republic has shown steady risk performance up to 2008, but two remaining areas for improvement are hazard monitoring and early warning systems. Jamaica has made uneven advances between 1990 and 2008, requiring significant improvements to achieve high performance levels and sustainability in risk management, as well as macroeconomic mitigation infrastructure. Trinidad and Tobago has the lowest risk management score of the 15 countries in the Latin American and Caribbean region as assessed by the IADB study in 2010, yet it has experienced an important vulnerability reduction. In sum, the results confirmed the high disaster risk management disparity in the Caribbean region.

Multi-scale analysis of active landslides using two-pass differential interferometry

Landslides are common features of the landscape of the north-central Apennine mountain range and cause frequent damage to human facilities and infrastructure. Most of these landslides move periodically with moderate velocities and, only after particular rainfall events, some accelerate abruptly. Synthetic aperture radar interferometry (InSAR) provides a particularly convenient method for studying deforming slopes. We use standard two-pass interferometry, taking advantage of the short revisit time of the Sentinel-1 satellites. In this paper we present the results of the InSAR analysis developed on several study areas in central and Northern Italian Apennines. The aims of the work described within the articles contained in this paper, concern: i) the potential of the standard two-pass interferometric technique for the recognition of active landslides; ii) the exploration of the potential related to the displacement time series resulting from a two-pass multiple time-scale InSAR analysis; iii) the evaluation of the possibility of making comparisons with climate forcing for cognitive and risk assessment purposes. Our analysis successfully identified more than 400 InSAR deformation signals (IDS) in the different study areas corresponding to active slope movements. The comparison between IDSs and thematic maps allowed us to identify the main characteristics of the slopes most prone to landslides. The analysis of displacement time series derived from monthly interferometric stacks or single 6-day interferograms allowed the establishment of landslide activity thresholds. This information, combined with the displacement time series, allowed the relationship between ground deformation and climate forcing to be successfully investigated. The InSAR data also gave access to the possibility of validating geographical warning systems and comparing the activity state of landslides with triggering probability thresholds.

Managing the risks of medical technology: Do hazard surveillance systems provide an early warning of medical device problems?

The Food and Drug Administration (FDA) is responsible for risk assessment and risk management in the post-market surveillance of the U.S. medical device industry. One of the FDA regulatory mechanisms, the Medical Device Reporting System (MDR) is an adverse event reporting system intended to provide the FDA with advance warning of device problems. It includes voluntary reporting for individuals, and mandatory reporting for device manufacturers. ^ In a study of alleged breast implant safety problems, this research examines the organizational processes by which the FDA gathers data on adverse events and uses adverse event reporting systems to assess and manage risk. The research reviews the literature on problem recognition, risk perception, and organizational learning to understand the influence highly publicized events may have on adverse event reporting. Understanding the influence of an environmental factor, such as publicity, on adverse event reporting can provide insight into the question of whether the FDA's adverse event reporting system operates as an early warning system for medical device problems. ^ The research focuses on two main questions. The first question addresses the relationship between publicity and the voluntary and mandatory reporting of adverse events. The second question examines whether government agencies make use of these adverse event reports. ^ Using quantitative and qualitative methods, a longitudinal study was conducted of the number and content of adverse event reports regarding breast implants filed with the FDA's medical device reporting system during 1985–1991. To assess variation in publicity over time, the print media were analyzed to identify articles related to breast implant failures. ^ The exploratory findings suggest that an increase in media activity is related to an increase in voluntary reporting, especially following periods of intense media coverage of the FDA. However, a similar relationship was not found between media activity and manufacturers' mandatory adverse event reporting. A review of government committee and agency reports on the FDA published during 1976–1996 produced little evidence to suggest that publicity or MDR information contributed to problem recognition, agenda setting, or the formulation of policy recommendations. ^ The research findings suggest that the reporting of breast implant problems to FDA may reflect the perceptions and concerns of the reporting groups, a barometer of the volume and content of media attention. ^

Effectiveness of safety belt warning and interlock systems. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Field evaluation of the Radar Control Systems (RCS) radar anti-collision warning system.

Partners for Advanced Transit and Highways, Berkeley, Calif.

Evaluation of warning and information systems. Part II, commercial radio advisories. Final report.

Federal Highway Administration, Washington, D.C.

Towards intelligent aquaculture. Development of an early Biological Warning System to monitor exposure to contaminants and fish welfare: from artificial vision to systems modelling

161 p.

Risk optimization of a steel frame communications tower subject to tornado winds

The structural engineering community in Brazil faces new challenges with the recent occurrence of high intensity tornados. Satellite surveillance data shows that the area covering the south-east of Brazil, Uruguay and some of Argentina is one of the world most tornado-prone areas, second only to the infamous tornado alley in central United States. The design of structures subject to tornado winds is a typical example of decision making in the presence of uncertainty. Structural design involves finding a good balance between the competing goals of safety and economy. This paper presents a methodology to find the optimum balance between these goals in the presence of uncertainty. In this paper, reliability-based risk optimization is used to find the optimal safety coefficient that minimizes the total expected cost of a steel frame communications tower, subject to extreme storm and tornado wind loads. The technique is not new, but it is applied to a practical problem of increasing interest to Brazilian structural engineers. The problem is formulated in the partial safety factor format used in current design codes, with all additional partial factor introduced to serve as optimization variable. The expected cost of failure (or risk) is defined as the product of a. limit state exceedance probability by a limit state exceedance cost. These costs include costs of repairing, rebuilding, and paying compensation for injury and loss of life. The total expected failure cost is the sum of individual expected costs over all failure modes. The steel frame communications, tower subject of this study has become very common in Brazil due to increasing mobile phone coverage. The study shows that optimum reliability is strongly dependent on the cost (or consequences) of failure. Since failure consequences depend oil actual tower location, it turn,,; out that different optimum designs should be used in different locations. Failure consequences are also different for the different parties involved in the design, construction and operation of the tower. Hence, it is important that risk is well understood by the parties involved, so that proper contracts call be made. The investigation shows that when non-structural terms dominate design costs (e.g, in residential or office buildings) it is not too costly to over-design; this observation is in agreement with the observed practice for non-optimized structural systems. In this situation, is much easier to loose money by under-design. When by under-design. When structural material cost is a significant part of design cost (e.g. concrete dam or bridge), one is likely to lose significantmoney by over-design. In this situation, a cost-risk-benefit optimization analysis is highly recommended. Finally, the study also shows that under time-varying loads like tornados, the optimum reliability is strongly dependent on the selected design life.

Evaluating design proposals for complex systems with work domain analysis

In this paper we propose a new framework for evaluating designs based on work domain analysis, the first phase of cognitive work analysis. We develop a rationale for a new approach to evaluation by describing the unique characteristics of complex systems and by showing that systems engineering techniques only partially accommodate these characteristics. We then present work domain analysis as a complementary framework for evaluation. We explain this technique by example by showing how the Australian Defence Force used work domain analysis to evaluate design proposals for a new system called Airborne Early Warning and Control. This case study also demonstrates that work domain analysis is a useful and feasible approach that complements standard techniques for evaluation and that promotes a central role for human factors professionals early in the system design and development process. Actual or potential applications of this research include the evaluation of designs for complex systems.