68 research outputs found
Environmental and Socio-Demographic Determinants of Dengue Fever in Colombo City, Sri Lanka
Dengue fever has increased exponentially in Sri Lanka, from 24.4 cases per 100,000 in 2003 to 165.3 per 100,000 population in 2013. Although early warning systems using predictor models have been previously developed in other settings, it is important to develop such models in each local setting. Further, the ability of these models to be applicable at smaller geographic units will enhance current vector control and disease surveillance measures. The aim of this paper was to identify environmental and socio-economic status (SES) risk factors that may predict dengue fever at the Gram Niladhari Divisions (GND) level (smallest administrative unit) in Colombo city, Sri Lanka. These factors included landcover classes, amount of vegetation, population density, water access and neighborhood SES as determined by roof type. A geographically weighted regression (GWR) was used to develop the prediction model. A total 55 GND units covering an area of 37 sq km were investigated. We found that GND units with decreased vegetation, higher built-up area, higher population density and poor access to tap-water supply were associated with high risk of dengue; the pertinent GND units were concentrated in the center of the city. This is the first study in Sri Lanka to include both environmental and socio-demographic factors in prediction models for dengue fever. The methodology may be useful in enhancing ongoing dengue fever control measures in the country, and to be extended to other countries in the region that have an increasing incidence of dengue fever
LINKING SEASONAL PREDICTIONS TO DECISION-MAKING AND DISASTER MANAGEMENT IN THE GREATER HORN OF AFRICA
Seventy-six participants, including experts from seven countries from the Greater Horn of Africa (GHA) and project coinvestigators from the United States, met to discuss experimental seasonal prediction models and products for the GHA, to engage decision-makers and users in the assessment of hydroclimatic information requirements, and to use feedback to build a framework to support decision-making and disaster management. In pre- and postworkshop surveys, workshop participants were asked how the utility of forecasts to decision-makers might be improved. Their recommendations are presente
Sintomatologia ansiosa e depressiva em famílias com filhos adolescentes: Qual o papel da diferenciação do self dos pais?
A literatura tem realçado o impacto da história familiar de psicopatologia no
desenvolvimento de perturbações emocionais nas gerações mais novas e a associação
entre o nível de diferenciação do self e diversos quadros clínicos (e.g., ansiosos e
depressivos). Com recurso a um desenho quantitativo transversal e a uma amostra de
104 tríades familiares (mãe, pai e filho/a adolescente), o presente estudo pretende
analisar: a associação entre a sintomatologia depressiva e ansiosa de mães e pais e
filhos/as adolescentes; e o papel mediador da diferenciação do self das mães e dos pais
na relação entre a sintomatologia depressiva e ansiosa de mães e de pais e a
sintomatologia depressiva e ansiosa dos/as filhos/as adolescentes. Os resultados
mostram que: a sintomatologia depressiva e ansiosa do pai e da mãe são preditoras do
nível de diferenciação do self do pai e da mãe; a relação entre a sintomatologia
depressiva da mãe e dos/as filhos/as é mediada pela diferenciação do self da mãe; e a
sintomatologia ansiosa da mãe é preditora da sintomatologia ansiosa dos/as filhos/as.
Apesar de os resultados sugerirem que a mãe tenha também um papel central na
transmissão de adversidade aos filhos, apontam para que a sintomatologia ansiosa e
depressiva de pais e filhos se associem de formas diferentes. Este estudo tem
implicações para a prática clínica e para a literatura na área da psicologia clínica e
psicologia da família, ao relevar o impacto da sintomatologia da mãe e do seu nível de
diferenciação do self no desenvolvimento de psicopatologia na adolescência.The literature has highlighted the impact of the family psychopathology history on the
development of psychopathology in the younger generations and the association
between the level of self differentiation and various clinical conditions (e.g., anxiety and
depression). Using a quantitative cross-sectional design and a sample of 104 family
triads (mother, father and adolescent child), the present study aims to analyze: the
association between depressive and anxious symptomatology of mothers and fathers
and adolescent children; and the mediating role of the self-differentiation of mothers
and fathers in the relationship between the depressive and anxious symptomatology of
mothers and fathers and the depressive and anxious symptomatology of the adolescent
children. The results show that: the depressive and anxious symptomatology of the
father and the mother are predictors of the level of differentiation of the self of the
father and the mother; the relationship between the depressive symptomatology of the
mother and the child is mediated by the differentiation of the mother's self; and the
anxious symptomatology of the mother is a predictor of the anxious symptomatology of
the child. Although the results suggest that the mother also has a central role in the
transmission of adversity to the children, they point out that the anxious and depressive
symptomatology of parents and children associate in different ways. This study has
implications for clinical practice and for literature in clinical psychology and family
psychology, by highlighting the impact of the mother's symptomatology and mothers’
level of self differentiation in the development of psychopathology in adolescence.Orientação: Ana Priost
Identifying and Addressing Land Surface Model Deficiencies with Data Assimilation
Land surface models (LSMs) encapsulate our understanding of terrestrial water and energy cycle physics and provide estimates of land surface states and fluxes when and where measurement gaps exist. Gaps in our understanding of the physics are a different issue. Data assimilation can address that issue both directly, through updating of prognostic model variables, or indirectly, when the simulated world conflicts with observation, necessitating adjustment of the model. Here we will focus on the latter case and present several examples, including (1) depth to bedrock adjustment to accommodate assimilated GRACE terrestrial water storage data; (2) steps to prevent immediate melting of assimilated snow cover; (3) irrigation's contribution to evapotranspiration; (4) lessons learned from soil moisture data assimilation; (5) the potential impact of satellite based runoff observatio
Fuel for growth and development
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115971/1/sign854.pd
A Regional Drought Monitoring and Outlook System for South Asia
AbstractThe Regional Drought Monitoring and Outlook System (RDMOS) is an operational service which produces reliable drought indicators for the south Asia region with a specific focus on Afghanistan, Bangladesh, Nepal, and Pakistan. The system incorporates climatic models with suitable Earth observation data and land surface models to produce drought indices—precipitation, temperature, soil moisture, evapotranspiration—and vegetation conditions at 10-day intervals for near realtime monitoring of droughts. The RDMOS also provides seasonal outlooks at four-month intervals to support drought management and preparedness processes
Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning
As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013
The Climate of the Mediterranean Region in Future Climate Projections
Future climate change over the Mediterranean area is investigated by means of climate model simulations covering the twenty-first century that take into account different anthropogenic greenhouse-gas-emission scenarios. This chapter first gives some new insights on these projections coming from the use of new methods, including the coupling at the regional scale of the atmospheric component to a Mediterranean Sea component. A synthesis of the expected changes of key aspects of the Mediterranean regional climate, obtained with a wide range of models and downscaling methods, is then presented. This includes an overview of not only expected changes in the mean climate and climate extremes but also possible changes in Mediterranean Sea temperature, salinity, circulation, water and heat budgets, and sea level. The chapter ends with some advanced results on the way to deal with uncertainties in climate projections and some discussion on the confidence that we can attribute to these projections
Ongoing Development of NASA's Global Land Data Assimilation System
NASA's Global Land Data Assimilation System (GLDAS) produces global fields of land surface states (e.g., soil moisture and temperature) and fluxes (e.g., latent heat flux and runoff) by driving offline land surface models with observation-based inputs, using the Land Information System (LIS) software. Since production began in 2001, GLDAS has supported more than 100 scientific investigations and applications. Some examples are GEWEX and NASA Energy and Water Cycle Study (NEWS) global water and energy budget analyses, interpretations of hydrologic data derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and forecast model initiation studies at NOAA and NASA. At the same time, the GLDAS team has continued improve results through the development of new modeling and data assimilation techniques. Here we describe several recent and ongoing innovations. These include global implementation of a runoff routing procedure, GRACE data assimilation, advanced snow cover assimilation, and irrigation modeling
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