On Constructing Ageing Rural Populations: "Capturing" the Grey Nomad

ERIC Educational Resources Information Center

Davies, Amanda

2011-01-01

The world's population is ageing, with forecasts predicting this ageing is likely to be particularly severe in the rural areas of more developed countries. These forecasts are developed from nationally aggregated census and survey data and assume spatial homogeneity in ageing. They also draw on narrow understandings of older people and construct…

Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model

NASA Astrophysics Data System (ADS)

Ibrahim, R. I.; Ngataman, N.; Abrisam, W. N. A. Wan Mohd

2017-09-01

Improvement in life expectancies has driven further declines in mortality. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all forecasting methods, the Lee-Carter model has been widely accepted by the actuarial community and Heligman-Pollard model has been widely used by researchers in modelling and forecasting future mortality. Therefore, this paper only focuses on Lee-Carter model and Heligman-Pollard model. The main objective of this paper is to investigate how accurately these two models will perform using Malaysian data. Since these models involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 8.0 (MATLAB 8.0) software will be used to estimate the parameters of the models. Autoregressive Integrated Moving Average (ARIMA) procedure is applied to acquire the forecasted parameters for both models as the forecasted mortality rates are obtained by using all the values of forecasted parameters. To investigate the accuracy of the estimation, the forecasted results will be compared against actual data of mortality rates. The results indicate that both models provide better results for male population. However, for the elderly female population, Heligman-Pollard model seems to underestimate to the mortality rates while Lee-Carter model seems to overestimate to the mortality rates.

A national econometric forecasting model of the dental sector.

PubMed Central

Feldstein, P J; Roehrig, C S

1980-01-01

The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, the proportion of the population covered by private dental insurance, the cost of hiring clericals and dental assistants, and relevant government policies. In a test of its reliability, the model forecast dental sector behavior quite accurately for the period 1971 through 1977. PMID:7461974

Stochastic Forecasting of Labor Supply and Population: An Integrated Model.

PubMed

Fuchs, Johann; Söhnlein, Doris; Weber, Brigitte; Weber, Enzo

2018-01-01

This paper presents a stochastic model to forecast the German population and labor supply until 2060. Within a cohort-component approach, our population forecast applies principal components analysis to birth, mortality, emigration, and immigration rates, which allows for the reduction of dimensionality and accounts for correlation of the rates. Labor force participation rates are estimated by means of an econometric time series approach. All time series are forecast by stochastic simulation using the bootstrap method. As our model also distinguishes between German and foreign nationals, different developments in fertility, migration, and labor participation could be predicted. The results show that even rising birth rates and high levels of immigration cannot break the basic demographic trend in the long run. An important finding from an endogenous modeling of emigration rates is that high net migration in the long run will be difficult to achieve. Our stochastic perspective suggests therefore a high probability of substantially decreasing the labor supply in Germany.

Forecasting the mortality rates of Indonesian population by using neural network

NASA Astrophysics Data System (ADS)

Safitri, Lutfiani; Mardiyati, Sri; Rahim, Hendrisman

2018-03-01

A model that can represent a problem is required in conducting a forecasting. One of the models that has been acknowledged by the actuary community in forecasting mortality rate is the Lee-Certer model. Lee Carter model supported by Neural Network will be used to calculate mortality forecasting in Indonesia. The type of Neural Network used is feedforward neural network aligned with backpropagation algorithm in python programming language. And the final result of this study is mortality rate in forecasting Indonesia for the next few years

Bayesian Forecasting Tool to Predict the Need for Antidote in Acute Acetaminophen Overdose.

PubMed

Desrochers, Julie; Wojciechowski, Jessica; Klein-Schwartz, Wendy; Gobburu, Jogarao V S; Gopalakrishnan, Mathangi

2017-08-01

Acetaminophen (APAP) overdose is the leading cause of acute liver injury in the United States. Patients with elevated plasma acetaminophen concentrations (PACs) require hepatoprotective treatment with N-acetylcysteine (NAC). These patients have been primarily risk-stratified using the Rumack-Matthew nomogram. Previous studies of acute APAP overdoses found that the nomogram failed to accurately predict the need for the antidote. The objectives of this study were to develop a population pharmacokinetic (PK) model for APAP following acute overdose and evaluate the utility of population PK model-based Bayesian forecasting in NAC administration decisions. Limited APAP concentrations from a retrospective cohort of acute overdosed subjects from the Maryland Poison Center were used to develop the population PK model and to investigate the effect of type of APAP products and other prognostic factors. The externally validated population PK model was used a prior for Bayesian forecasting to predict the individual PK profile when one or two observed PACs were available. The utility of Bayesian forecasted APAP concentration-time profiles inferred from one (first) or two (first and second) PAC observations were also tested in their ability to predict the observed NAC decisions. A one-compartment model with first-order absorption and elimination adequately described the data with single activated charcoal and APAP products as significant covariates on absorption and bioavailability. The Bayesian forecasted individual concentration-time profiles had acceptable bias (6.2% and 9.8%) and accuracy (40.5% and 41.9%) when either one or two PACs were considered, respectively. The sensitivity and negative predictive value of the Bayesian forecasted NAC decisions using one PAC were 84% and 92.6%, respectively. The population PK analysis provided a platform for acceptably predicting an individual's concentration-time profile following acute APAP overdose with at least one PAC, and the individual's covariate profile, and can potentially be used for making early NAC administration decisions. © 2017 Pharmacotherapy Publications, Inc.

Nambe Pueblo Water Budget and Forecasting model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brainard, James Robert

2009-10-01

This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Watermore » Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.« less

Forecasting the use of elderly care: a static micro-simulation model.

PubMed

Eggink, Evelien; Woittiez, Isolde; Ras, Michiel

2016-07-01

This paper describes a model suitable for forecasting the use of publicly funded long-term elderly care, taking into account both ageing and changes in the health status of the population. In addition, the impact of socioeconomic factors on care use is included in the forecasts. The model is also suitable for the simulation of possible implications of some specific policy measures. The model is a static micro-simulation model, consisting of an explanatory model and a population model. The explanatory model statistically relates care use to individual characteristics. The population model mimics the composition of the population at future points in time. The forecasts of care use are driven by changes in the composition of the population in terms of relevant characteristics instead of dynamics at the individual level. The results show that a further 37 % increase in the use of elderly care (from 7 to 9 % of the Dutch 30-plus population) between 2008 and 2030 can be expected due to a further ageing of the population. However, the use of care is expected to increase less than if it were based on the increasing number of elderly only (+70 %), due to decreasing disability levels and increasing levels of education. As an application of the model, we simulated the effects of restricting access to residential care to elderly people with severe physical disabilities. The result was a lower growth of residential care use (32 % instead of 57 %), but a somewhat faster growth in the use of home care (35 % instead of 32 %).

Socioeconomic Forecasting Model for the Tri-County Regional Planning Commission

DOT National Transportation Integrated Search

1997-01-01

Socioeconomic data is a critical input to transportation planning and travel demand forecasting. Accurate estimates of existing population, incomes, employment and other socioeconomic characteristics are necessary for meaningful calibration of a trav...

Improving socioeconomic land use forecasting for medium-sized metropolitan organizations in Virginia.

DOT National Transportation Integrated Search

2008-01-01

Socioeconomic forecasts are the foundation for long range travel demand modeling, projecting variables such as population, households, employment, and vehicle ownership. In Virginia, metropolitan planning organizations (MPOs) develop socioeconomic fo...

Forecasting consequences of changing sea ice availability for Pacific walruses

USGS Publications Warehouse

Udevitz, Mark S.; Jay, Chadwick V.; Taylor, Rebecca; Fischbach, Anthony S.; Beatty, William S.; Noren, Shawn R.

2017-01-01

The accelerating rate of anthropogenic alteration and disturbance of environments has increased the need for forecasting effects of environmental change on fish and wildlife populations. Models linking projections of environmental change with behavioral responses and bioenergetic effects can provide a basis for these forecasts. There is particular interest in forecasting effects of projected reductions in sea ice availability on Pacific walruses (Odobenus rosmarus divergens). Declining extent of summer sea ice in the Chukchi Sea has caused Pacific walruses to increase use of coastal haulouts and decrease use of more productive offshore feeding areas. Such climate-induced changes in distribution and behavior could ultimately affect the status of the population. We developed behavioral models to relate changes in sea ice availability to adult female walrus movements and activity levels, and adapted previously developed bioenergetics models to relate those activity levels to energy requirements and the ability to meet those requirements. We then linked these models to general circulation model projections of future ice availability to forecast autumn body condition for female walruses during mid- and late-century time periods. Our results suggest that as sea ice becomes less available in the Chukchi Sea, female walruses will spend more time in the southwestern region of that sea, less time resting, and less time foraging. Median forecasted autumn body masses were 7–12% lower in future scenarios than during recent times, but posterior distributions broadly overlapped and median forecasted seasonal mass losses (15–34%) were comparable to seasonal mass losses routinely experienced by other pinnipeds. These seasonal reductions in body condition would be unlikely to result in demographic effects, but if walruses were unable to rebuild endogenous reserves while wintering in the Bering Sea, cumulative effects could have implications for reproduction and survival, ultimately affecting the status of the Pacific walrus population. Our approach provides a general framework for forecasting consequences of the broad range of environmental changes and anthropogenic disturbances that may affect bioenergetics through behavioral responses or changes in prey availability.

SEASAT economic assessment. Volume 8: Ocean fishing case study. [economic benefits of SEASAT satellites to ocean fishing industries in the United States and Canada

NASA Technical Reports Server (NTRS)

1975-01-01

The potential application of SEASAT data with regard to ocean fisheries is discussed. Tracking fish populations, indirect assistance in forecasting expected populations and assistance to fishing fleets in avoiding costs incurred due to adverse weather through improved ocean conditions forecasts were investigated. Case studies on fisheries in the United States and Canada are cited.

[Forecast of costs of ecodependent cancer treatment for the development of management decisions].

PubMed

Krasovskiy, V O

2014-01-01

The methodical approach for probabilistic forecasting and differentiation of treatment of costs of ecodependent cancer cases has been elaborated. The modality is useful in the organization of medical aid to cancer patients, in developing management decisions for the reduction the occupational load on the population, as well as in solutions problems in compensation to the population economic and social loss from industrial plants.

Forecasting wildlife response to rapid warming in the Alaskan Arctic

USGS Publications Warehouse

Van Hemert, Caroline R.; Flint, Paul L.; Udevitz, Mark S.; Koch, Joshua C.; Atwood, Todd C.; Oakley, Karen L.; Pearce, John M.

2015-01-01

Arctic wildlife species face a dynamic and increasingly novel environment because of climate warming and the associated increase in human activity. Both marine and terrestrial environments are undergoing rapid environmental shifts, including loss of sea ice, permafrost degradation, and altered biogeochemical fluxes. Forecasting wildlife responses to climate change can facilitate proactive decisions that balance stewardship with resource development. In this article, we discuss the primary and secondary responses to physical climate-related drivers in the Arctic, associated wildlife responses, and additional sources of complexity in forecasting wildlife population outcomes. Although the effects of warming on wildlife populations are becoming increasingly well documented in the scientific literature, clear mechanistic links are often difficult to establish. An integrated science approach and robust modeling tools are necessary to make predictions and determine resiliency to change. We provide a conceptual framework and introduce examples relevant for developing wildlife forecasts useful to management decisions.

Forecasting differences in life expectancy by education.

PubMed

van Baal, Pieter; Peters, Frederik; Mackenbach, Johan; Nusselder, Wilma

2016-07-01

Forecasts of life expectancy (LE) have fuelled debates about the sustainability and dependability of pension and healthcare systems. Of relevance to these debates are inequalities in LE by education. In this paper, we present a method of forecasting LE for different educational groups within a population. As a basic framework we use the Li-Lee model that was developed to forecast mortality coherently for different groups. We adapted this model to distinguish between overall, sex-specific, and education-specific trends in mortality, and extrapolated these time trends in a flexible manner. We illustrate our method for the population aged 65 and over in the Netherlands, using several data sources and spanning different periods. The results suggest that LE is likely to increase for all educational groups, but that differences in LE between educational groups will widen. Sensitivity analyses illustrate the advantages of our proposed method.

[Demography perspectives and forecasts of the demand for electricity].

PubMed

Roy, L; Guimond, E

1995-01-01

"Demographic perspectives form an integral part in the development of electric load forecasts. These forecasts in turn are used to justify the addition and repair of generating facilities that will supply power in the coming decades. The goal of this article is to present how demographic perspectives are incorporated into the electric load forecasting in Quebec. The first part presents the methods, hypotheses and results of population and household projections used by Hydro-Quebec in updating its latest development plan. The second section demonstrates applications of such demographic projections for forecasting the electric load, with a focus on the residential sector." (SUMMARY IN ENG AND SPA) excerpt

A statistical approach to quasi-extinction forecasting.

PubMed

Holmes, Elizabeth Eli; Sabo, John L; Viscido, Steven Vincent; Fagan, William Fredric

2007-12-01

Forecasting population decline to a certain critical threshold (the quasi-extinction risk) is one of the central objectives of population viability analysis (PVA), and such predictions figure prominently in the decisions of major conservation organizations. In this paper, we argue that accurate forecasting of a population's quasi-extinction risk does not necessarily require knowledge of the underlying biological mechanisms. Because of the stochastic and multiplicative nature of population growth, the ensemble behaviour of population trajectories converges to common statistical forms across a wide variety of stochastic population processes. This paper provides a theoretical basis for this argument. We show that the quasi-extinction surfaces of a variety of complex stochastic population processes (including age-structured, density-dependent and spatially structured populations) can be modelled by a simple stochastic approximation: the stochastic exponential growth process overlaid with Gaussian errors. Using simulated and real data, we show that this model can be estimated with 20-30 years of data and can provide relatively unbiased quasi-extinction risk with confidence intervals considerably smaller than (0,1). This was found to be true even for simulated data derived from some of the noisiest population processes (density-dependent feedback, species interactions and strong age-structure cycling). A key advantage of statistical models is that their parameters and the uncertainty of those parameters can be estimated from time series data using standard statistical methods. In contrast for most species of conservation concern, biologically realistic models must often be specified rather than estimated because of the limited data available for all the various parameters. Biologically realistic models will always have a prominent place in PVA for evaluating specific management options which affect a single segment of a population, a single demographic rate, or different geographic areas. However, for forecasting quasi-extinction risk, statistical models that are based on the convergent statistical properties of population processes offer many advantages over biologically realistic models.

The Impact of Implementing a Demand Forecasting System into a Low-Income Country’s Supply Chain

PubMed Central

Mueller, Leslie E.; Haidari, Leila A.; Wateska, Angela R.; Phillips, Roslyn J.; Schmitz, Michelle M.; Connor, Diana L.; Norman, Bryan A.; Brown, Shawn T.; Welling, Joel S.; Lee, Bruce Y.

2016-01-01

OBJECTIVE To evaluate the potential impact and value of applications (e.g., ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country’s vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS Using our software, HERMES, we generated a detailed discrete event simulation model of Niger’s entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION Demand forecasting systems have the potential to greatly improve vaccine demand fulfillment, and decrease logistics cost/dose when implemented with storage and transportation increases direct vaccines. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. PMID:27219341

The impact of implementing a demand forecasting system into a low-income country's supply chain.

PubMed

Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

2016-07-12

To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright © 2016 Elsevier Ltd. All rights reserved.

Does Specification Matter? Experiments with Simple Multiregional Probabilistic Population Projections

PubMed Central

Raymer, James; Abel, Guy J.; Rogers, Andrei

2012-01-01

Population projection models that introduce uncertainty are a growing subset of projection models in general. In this paper, we focus on the importance of decisions made with regard to the model specifications adopted. We compare the forecasts and prediction intervals associated with four simple regional population projection models: an overall growth rate model, a component model with net migration, a component model with in-migration and out-migration rates, and a multiregional model with destination-specific out-migration rates. Vector autoregressive models are used to forecast future rates of growth, birth, death, net migration, in-migration and out-migration, and destination-specific out-migration for the North, Midlands and South regions in England. They are also used to forecast different international migration measures. The base data represent a time series of annual data provided by the Office for National Statistics from 1976 to 2008. The results illustrate how both the forecasted subpopulation totals and the corresponding prediction intervals differ for the multiregional model in comparison to other simpler models, as well as for different assumptions about international migration. The paper ends end with a discussion of our results and possible directions for future research. PMID:23236221

The Texas Children's Hospital immunization forecaster: conceptualization to implementation.

PubMed

Cunningham, Rachel M; Sahni, Leila C; Kerr, G Brady; King, Laura L; Bunker, Nathan A; Boom, Julie A

2014-12-01

Immunization forecasting systems evaluate patient vaccination histories and recommend the dates and vaccines that should be administered. We described the conceptualization, development, implementation, and distribution of a novel immunization forecaster, the Texas Children's Hospital (TCH) Forecaster. In 2007, TCH convened an internal expert team that included a pediatrician, immunization nurse, software engineer, and immunization subject matter experts to develop the TCH Forecaster. Our team developed the design of the model, wrote the software, populated the Excel tables, integrated the software, and tested the Forecaster. We created a table of rules that contained each vaccine's recommendations, minimum ages and intervals, and contraindications, which served as the basis for the TCH Forecaster. We created 15 vaccine tables that incorporated 79 unique dose states and 84 vaccine types to operationalize the entire United States recommended immunization schedule. The TCH Forecaster was implemented throughout the TCH system, the Indian Health Service, and the Virginia Department of Health. The TCH Forecast Tester is currently being used nationally. Immunization forecasting systems might positively affect adherence to vaccine recommendations. Efforts to support health care provider utilization of immunization forecasting systems and to evaluate their impact on patient care are needed.

The ecological forecast horizon, and examples of its uses and determinants

PubMed Central

Petchey, Owen L; Pontarp, Mikael; Massie, Thomas M; Kéfi, Sonia; Ozgul, Arpat; Weilenmann, Maja; Palamara, Gian Marco; Altermatt, Florian; Matthews, Blake; Levine, Jonathan M; Childs, Dylan Z; McGill, Brian J; Schaepman, Michael E; Schmid, Bernhard; Spaak, Piet; Beckerman, Andrew P; Pennekamp, Frank; Pearse, Ian S; Vasseur, David

2015-01-01

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development. PMID:25960188

Modeling and Forecasting Mortality With Economic Growth: A Multipopulation Approach.

PubMed

Boonen, Tim J; Li, Hong

2017-10-01

Research on mortality modeling of multiple populations focuses mainly on extrapolating past mortality trends and summarizing these trends by one or more common latent factors. This article proposes a multipopulation stochastic mortality model that uses the explanatory power of economic growth. In particular, we extend the Li and Lee model (Li and Lee 2005) by including economic growth, represented by the real gross domestic product (GDP) per capita, to capture the common mortality trend for a group of populations with similar socioeconomic conditions. We find that our proposed model provides a better in-sample fit and an out-of-sample forecast performance. Moreover, it generates lower (higher) forecasted period life expectancy for countries with high (low) GDP per capita than the Li and Lee model.

Project 1990: Educational Planning at the Metropolitan Level.

ERIC Educational Resources Information Center

Swanson, Austin D.; Lamitie, Robert E.

This paper describes a project designed to provide educational decisionmakers with projections of and forecasts about future metropolitan conditions and problems, and information about the implications of alternative ways of solving metropolitan problems. Project components included (1) population and economic projections and forecasts, (2)…

Forecasting extinction risk with nonstationary matrix models.

PubMed

Gotelli, Nicholas J; Ellison, Aaron M

2006-02-01

Matrix population growth models are standard tools for forecasting population change and for managing rare species, but they are less useful for predicting extinction risk in the face of changing environmental conditions. Deterministic models provide point estimates of lambda, the finite rate of increase, as well as measures of matrix sensitivity and elasticity. Stationary matrix models can be used to estimate extinction risk in a variable environment, but they assume that the matrix elements are randomly sampled from a stationary (i.e., non-changing) distribution. Here we outline a method for using nonstationary matrix models to construct realistic forecasts of population fluctuation in changing environments. Our method requires three pieces of data: (1) field estimates of transition matrix elements, (2) experimental data on the demographic responses of populations to altered environmental conditions, and (3) forecasting data on environmental drivers. These three pieces of data are combined to generate a series of sequential transition matrices that emulate a pattern of long-term change in environmental drivers. Realistic estimates of population persistence and extinction risk can be derived from stochastic permutations of such a model. We illustrate the steps of this analysis with data from two populations of Sarracenia purpurea growing in northern New England. Sarracenia purpurea is a perennial carnivorous plant that is potentially at risk of local extinction because of increased nitrogen deposition. Long-term monitoring records or models of environmental change can be used to generate time series of driver variables under different scenarios of changing environments. Both manipulative and natural experiments can be used to construct a linking function that describes how matrix parameters change as a function of the environmental driver. This synthetic modeling approach provides quantitative estimates of extinction probability that have an explicit mechanistic basis.

Long-term population cycles in human societies.

PubMed

Turchin, Peter

2009-04-01

Human population dynamics are usually conceptualized as either boundless growth or growth to an equilibrium. The implicit assumption underlying these paradigms is that any feedback processes regulating population density, if they exist, operate on a fast-time-scale, and therefore we do not expect to observe population oscillations in human population numbers. This review asks, are population processes in historical and prehistorical human populations characterized by second-order feedback loops, that is, regulation involving lags? If yes, then the implications for forecasting future population change are obvious--what may appear as inexplicable, exogenously driven reverses in population trends may actually be a result of feedbacks operating with substantial time lags. This survey of a variety of historical and archeological data indicates that slow oscillations in population numbers, with periods of roughly two to three centuries, are observed in a number of world regions and historical periods. Next, a potential explanation for this pattern, the demographic-structural theory, is discussed. Finally, the implications of these results for global population forecasts is discussed.

Forecasting Tools Point to Fishing Hotspots

NASA Technical Reports Server (NTRS)

2009-01-01

Private weather forecaster WorldWinds Inc. of Slidell, Louisiana has employed satellite-gathered oceanic data from Marshall Space Flight Center to create a service that is every fishing enthusiast s dream. The company's FishBytes system uses information about sea surface temperature and chlorophyll levels to forecast favorable conditions for certain fish populations. Transmitting the data to satellite radio subscribers, FishBytes provides maps that guide anglers to the areas they are most likely to make their favorite catch.

The evolutionary time machine: forecasting how populations can adapt to changing environments using dormant propagules

PubMed Central

Orsini, Luisa; Schwenk, Klaus; De Meester, Luc; Colbourne, John K.; Pfrender, Michael E.; Weider, Lawrence J.

2013-01-01

Evolutionary changes are determined by a complex assortment of ecological, demographic and adaptive histories. Predicting how evolution will shape the genetic structures of populations coping with current (and future) environmental challenges has principally relied on investigations through space, in lieu of time, because long-term phenotypic and molecular data are scarce. Yet, dormant propagules in sediments, soils and permafrost are convenient natural archives of population-histories from which to trace adaptive trajectories along extended time periods. DNA sequence data obtained from these natural archives, combined with pioneering methods for analyzing both ecological and population genomic time-series data, are likely to provide predictive models to forecast evolutionary responses of natural populations to environmental changes resulting from natural and anthropogenic stressors, including climate change. PMID:23395434

Predictable Unpredictability: the Problem with Basing Medicare Policy on Long-Term Financial Forecasting.

PubMed

Glied, Sherry; Zaylor, Abigail

2015-07-01

The authors assess how Medicare financing and projections of future costs have changed since 2000. They also assess the impact of legislative reforms on the sources and levels of financing and compare cost forecasts made at different times. Although the aging U.S. population and rising health care costs are expected to increase the share of gross domestic product devoted to Medicare, changes made in the program over the past decade have helped stabilize Medicare's financial outlook--even as benefits have been expanded. Long-term forecasting uncertainty should make policymakers and beneficiaries wary of dramatic changes to the program in the near term that are intended to alter its long-term forecast: the range of error associated with cost forecasts rises as the forecast window lengthens. Instead, policymakers should focus on the immediate policy window, taking steps to reduce the current burden of Medicare costs by containing spending today.

Defining conservation priorities using fragmentation forecasts

Treesearch

David Wear; John Pye; Kurt H. Riitters

2004-01-01

Methods are developed for forecasting the effects of population and economic growth on the distribution of interior forest habitat. An application to the southeastern United States shows that models provide significant explanatory power with regard to the observed distribution of interior forest. Estimates for economic and biophysical variables are significant and...

Forecasting in the presence of expectations

NASA Astrophysics Data System (ADS)

Allen, R.; Zivin, J. G.; Shrader, J.

2016-05-01

Physical processes routinely influence economic outcomes, and actions by economic agents can, in turn, influence physical processes. This feedback creates challenges for forecasting and inference, creating the potential for complementarity between models from different academic disciplines. Using the example of prediction of water availability during a drought, we illustrate the potential biases in forecasts that only take part of a coupled system into account. In particular, we show that forecasts can alter the feedbacks between supply and demand, leading to inaccurate prediction about future states of the system. Although the example is specific to drought, the problem of feedback between expectations and forecast quality is not isolated to the particular model-it is relevant to areas as diverse as population assessments for conservation, balancing the electrical grid, and setting macroeconomic policy.

Contrasting growth forecasts across the geographical range of Scots pine due to altitudinal and latitudinal differences in climatic sensitivity.

PubMed

Matías, Luis; Linares, Juan C; Sánchez-Miranda, Ángela; Jump, Alistair S

2017-10-01

Ongoing changes in global climate are altering ecological conditions for many species. The consequences of such changes are typically most evident at the edge of a species' geographical distribution, where differences in growth or population dynamics may result in range expansions or contractions. Understanding population responses to different climatic drivers along wide latitudinal and altitudinal gradients is necessary in order to gain a better understanding of plant responses to ongoing increases in global temperature and drought severity. We selected Scots pine (Pinus sylvestris L.) as a model species to explore growth responses to climatic variability (seasonal temperature and precipitation) over the last century through dendrochronological methods. We developed linear models based on age, climate and previous growth to forecast growth trends up to year 2100 using climatic predictions. Populations were located at the treeline across a latitudinal gradient covering the northern, central and southernmost populations and across an altitudinal gradient at the southern edge of the distribution (treeline, medium and lower elevations). Radial growth was maximal at medium altitude and treeline of the southernmost populations. Temperature was the main factor controlling growth variability along the gradients, although the timing and strength of climatic variables affecting growth shifted with latitude and altitude. Predictive models forecast a general increase in Scots pine growth at treeline across the latitudinal distribution, with southern populations increasing growth up to year 2050, when it stabilizes. The highest responsiveness appeared at central latitude, and moderate growth increase is projected at the northern limit. Contrastingly, the model forecasted growth declines at lowland-southern populations, suggesting an upslope range displacement over the coming decades. Our results give insight into the geographical responses of tree species to climate change and demonstrate the importance of incorporating biogeographical variability into predictive models for an accurate prediction of species dynamics as climate changes. © 2017 John Wiley & Sons Ltd.

Forecasting patient outcomes in the management of hyperlipidemia.

PubMed

Brier, K L; Tornow, J J; Ries, A J; Weber, M P; Downs, J R

1999-03-22

To forecast adult patient outcomes in the management of hyperlipidemia using adult National Health and Examination Survey III (NHANES III) population statistics and National Cholesterol Education Program (NCEP) guidelines for goals of therapy. Review of the hyperlipidemia drug therapy English-language medical literature with emphasis on randomized controlled trials of more than 6 weeks' duration published in the last 7 years, product package inserts, US Food and Drug Administration submission information, and NHANES III population statistics. Data were extracted from studies of lipid-lowering therapy to modify low-density lipoprotein (LDL) levels for primary and secondary prevention of coronary heart disease. The data that were evaluated included sample size, study design, therapeutic intervention, length of study, percentage change in LDL levels, and patient demographics. Cumulative frequency curves of the LDL distribution among the US adult population were constructed. The mean efficacy of drug therapy from qualified studies was used to extrapolate the percentage of the population expected to respond to the intervention and to forecast the patient outcome. A useful tool for clinicians was constructed to approximate the percentage of patients, based on risk stratification, who would reach NCEP target goal after a given pharmacotherapeutic intervention to decrease LDL levels.

Rebuttal of "Polar bear population forecasts: a public-policy forecasting audit"

USGS Publications Warehouse

Amstrup, Steven C.; Caswell, Hal; DeWeaver, Eric; Stirling, Ian; Douglas, David C.; Marcot, Bruce G.; Hunter, Christine M.

2009-01-01

Observed declines in the Arctic sea ice have resulted in a variety of negative effects on polar bears (Ursus maritimus). Projections for additional future declines in sea ice resulted in a proposal to list polar bears as a threatened species under the United States Endangered Species Act. To provide information for the Department of the Interior's listing-decision process, the US Geological Survey (USGS) produced a series of nine research reports evaluating the present and future status of polar bears throughout their range. In response, Armstrong et al. [Armstrong, J. S., K. C. Green, W. Soon. 2008. Polar bear population forecasts: A public-policy forecasting audit. Interfaces 38(5) 382–405], which we will refer to as AGS, performed an audit of two of these nine reports. AGS claimed that the general circulation models upon which the USGS reports relied were not valid forecasting tools, that USGS researchers were not objective or lacked independence from policy decisions, that they did not utilize all available information in constructing their forecasts, and that they violated numerous principles of forecasting espoused by AGS. AGS (p. 382) concluded that the two USGS reports were "unscientific and inconsequential to decision makers." We evaluate the AGS audit and show how AGS are mistaken or misleading on every claim. We provide evidence that general circulation models are useful in forecasting future climate conditions and that corporate and government leaders are relying on these models to do so. We clarify the strict independence of the USGS from the listing decision. We show that the allegations of failure to follow the principles of forecasting espoused by AGS are either incorrect or are based on misconceptions about the Arctic environment, polar bear biology, or statistical and mathematical methods. We conclude by showing that the AGS principles of forecasting are too ambiguous and subjective to be used as a reliable basis for auditing scientific investigations. In summary, we show that the AGS audit offers no valid criticism of the USGS conclusion that global warming poses a serious threat to the future welfare of polar bears and that it only serves to distract from reasoned public-policy debate.

ASSESSMENT OF THE WINTER-TIME PERFORMANCE OF DEVELOPMENTAL PARTICULATE MATTER FORECASTS WITH THE ETA-CMAQ MODELING SYSTEM

EPA Science Inventory

It is desirable for local air quality agencies to accurately forecast tropospheric PM_2.5 concentrations to alert the sensitive population of the onset, severity and duration of unhealthy air, and to encourage the public and industry to reduce emissions-producing activi...

Forecasting Social Trends as a Basis for Formulating Educational Policy.

ERIC Educational Resources Information Center

Lewis, Arthur J.

The paper describes how information regarding future trends is collected and made available to educational policy makers. Focusing on educational implications of social and population trends, the paper is based on data derived from use of trend forecasting by educational policy makers in Florida and other southeastern states. The document is…

Relationship of physiography and snow area to stream discharge. [Kings River Watershed, California

NASA Technical Reports Server (NTRS)

Mccuen, R. H. (Principal Investigator)

1979-01-01

The author has identified the following significant results. A comparison of snowmelt runoff models shows that the accuracy of the Tangborn model and regression models is greater if the test data falls within the range of calibration than if the test data lies outside the range of calibration data. The regression models are significantly more accurate for forecasts of 60 days or more than for shorter prediction periods. The Tangborn model is more accurate for forecasts of 90 days or more than for shorter prediction periods. The Martinec model is more accurate for forecasts of one or two days than for periods of 3,5,10, or 15 days. Accuracy of the long-term models seems to be independent of forecast data. The sufficiency of the calibration data base is a function not only of the number of years of record but also of the accuracy with which the calibration years represent the total population of data years. Twelve years appears to be a sufficient length of record for each of the models considered, as long as the twelve years are representative of the population.

Assessment of seasonal soil moisture forecasts over Southern South America with emphasis on dry and wet events

NASA Astrophysics Data System (ADS)

Spennemann, Pablo; Rivera, Juan Antonio; Osman, Marisol; Saulo, Celeste; Penalba, Olga

2017-04-01

The importance of forecasting extreme wet and dry conditions from weeks to months in advance relies on the need to prevent considerable socio-economic losses, mainly in regions of large populations and where agriculture is a key value for the economies, like Southern South America (SSA). Therefore, to improve the understanding of the performance and uncertainties of seasonal soil moisture and precipitation forecasts over SSA, this study aims to: 1) perform a general assessment of the Climate Forecast System version-2 (CFSv2) soil moisture and precipitation forecasts; and 2) evaluate the CFSv2 ability to represent an extreme drought event merging observations with forecasted Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Anomalies (SSMA) based on GLDAS-2.0 simulations. Results show that both SPI and SSMA forecast skill are regionally and seasonally dependent. In general a fast degradation of the forecasts skill is observed as the lead time increases with no significant metrics for forecast lead times longer than 2 months. Based on the assessment of the 2008-2009 extreme drought event it is evident that the CFSv2 forecasts have limitations regarding the identification of drought onset, duration, severity and demise, considering both meteorological (SPI) and agricultural (SSMA) drought conditions. These results have some implications upon the use of seasonal forecasts to assist agricultural practices in SSA, given that forecast skill is still too low to be useful for lead times longer than 2 months.

New forecasting methodology indicates more disease and earlier mortality ahead for today's younger Americans.

PubMed

Reither, Eric N; Olshansky, S Jay; Yang, Yang

2011-08-01

Traditional methods of projecting population health statistics, such as estimating future death rates, can give inaccurate results and lead to inferior or even poor policy decisions. A new "three-dimensional" method of forecasting vital health statistics is more accurate because it takes into account the delayed effects of the health risks being accumulated by today's younger generations. Applying this forecasting technique to the US obesity epidemic suggests that future death rates and health care expenditures could be far worse than currently anticipated. We suggest that public policy makers adopt this more robust forecasting tool and redouble efforts to develop and implement effective obesity-related prevention programs and interventions.

Long-term forecasting of anesthesia workload in operating rooms from changes in a hospital's local population can be inaccurate.

PubMed

Masursky, Danielle; Dexter, Franklin; O'Leary, Colleen E; Applegeet, Carol; Nussmeier, Nancy A

2008-04-01

Anesthesia department planning depends on forecasting future demand for perioperative services. Little is known about long-range forecasting of anesthesia workload. We studied operating room (OR) times at Hospital A over 16 yr (1991-2006), anesthesia times at Hospital B over 26 yr (1981-2006), and cases at Hospital C over 13 yr (1994-2006). Each hospital is >100 yr old and is located in a US city with other hospitals that are >50 yr old. Hospitals A and B are the sole University hospitals in their metropolitan statistical areas (and many counties beyond). Hospital C is the sole tertiary hospital for >375 km. Each hospital's choice of a measure of anesthesia work to be analyzed was likely unimportant, as the annual hours of anesthesia correlated highly both with annual numbers of cases (r = 0.98) and with American Society of Anesthesiologist's Relative Value Guide units of work (r = 0.99). Despite a 2% decline in the local population, the hours of OR time at Hospital A increased overall (Pearson r = -0.87, P < 0.001) and for children (r = -0.84). At Hospital B, there was a strong positive correlation between population and hours of anesthesia (r = 0.97, P < 0.001), but not between annual increases in population and workload (r = -0.18). At Hospital C, despite a linear increase in population, the annual numbers of cases increased, declined with opening of two outpatient surgery facilities, and then stabilized. The predictive value of local personal income was low. In contrast, the annual increases in the hours of OR time and anesthesia could be modeled using simple time series methods. Although growth of the elderly population is a simple justification for building more ORs, managers should be cautious in arguing for strategic changes in capacity at individual hospitals based on future changes in the national age-adjusted population. Local population can provide little value in forecasting future anesthesia workloads at individual hospitals. In addition, anesthesia groups and hospital administrators should not focus on quarterly changes in workload, because workload can vary widely, despite consistent patterns over decades. To facilitate long-range planning, anesthesia groups and hospitals should save their billing and OR time data, display it graphically over years, and supplement with corresponding forecasting methods (e.g., staff an additional OR when an upper prediction bound of workload per OR exceeds a threshold).

Forecast of the number of patients with end-stage renal disease in the United States to the year 2010.

PubMed

Xue, J L; Ma, J Z; Louis, T A; Collins, A J

2001-12-01

As the United States end-stage renal disease (ESRD) program enters the new millennium, the continued growth of the ESRD population poses a challenge for policy makers, health care providers, and financial planners. To assist in future planning for the ESRD program, the growth of patient numbers and Medicare costs was forecasted to the year 2010 by modeling of historical data from 1982 through 1997. A stepwise autoregressive method and exponential smoothing models were used. The forecasting models for ESRD patient numbers demonstrated mean errors of -0.03 to 1.03%, relative to the observed values. The model for Medicare payments demonstrated -0.12% mean error. The R(2) values for the forecasting models ranged from 99.09 to 99.98%. On the basis of trends in patient numbers, this forecast projects average annual growth of the ESRD populations of approximately 4.1% for new patients, 6.4% for long-term ESRD patients, 7.1% for dialysis patients, 6.1% for patients with functioning transplants, and 8.2% for patients on waiting lists for transplants, as well as 7.7% for Medicare expenditures. The numbers of patients with ESRD in 2010 are forecasted to be 129,200 +/- 7742 (95% confidence limits) new patients, 651,330 +/- 15,874 long-term ESRD patients, 520,240 +/- 25,609 dialysis patients, 178,806 +/- 4349 patients with functioning transplants, and 95,550 +/- 5478 patients on waiting lists. The forecasted Medicare expenditures are projected to increase to $28.3 +/- 1.7 billion by 2010. These projections are subject to many factors that may alter the actual growth, compared with the historical patterns. They do, however, provide a basis for discussing the future growth of the ESRD program and how the ESRD community can meet the challenges ahead.

The future of death in America

PubMed Central

King, Gary; Soneji, Samir

2013-01-01

Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public and private pensions, and health care financing systems. In part because existing methods forecast less accurately when based on more information, most forecasts are still based on simple linear extrapolations that ignore known biological risk factors and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too. PMID:24696636

Social Effects of Prospective Population Changes in the United States.

ERIC Educational Resources Information Center

Kirk, Dudley

Unlike many population forecasts, the thesis of this paper is that present and prospective effects of population growth in the United States have been exaggerated in comparison with other aspects of population change. The effects of national population growth have been confused with those of growing affluence, changing technology, and…

State of Washington Population Trends, 1977. Washington State Information Report.

ERIC Educational Resources Information Center

Washington State Office of Program Planning and Fiscal Management, Olympia.

As of April 1, 1977, Washington's population was estimated at 3,661,975--an increase of 248,725 since 1970. Prepared yearly, this report presents data on the official April 1 population estimates for cities, towns, and counties; components of population change; planned population forecasting activities; procedures which help make the housing unit…

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

Spatial forecast of landslides in three gorges based on spatial data mining.

PubMed

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

PubMed Central

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods. PMID:22573999

Rebuttal of "Polar bear population forecasts: a public-policy forecasting audit"

Treesearch

Steven C. Amstrup; Hal Caswell; Eric DeWeaver; Ian Stirling; David C. Douglas; Bruce G. Marcot; Christine M. Hunter

2009-01-01

Observed declines in the Arctic sea ice have resulted in a variety of negative effects on polar bears (Ursus maritimus). Projections for additional future declines in sea ice resulted in a proposal to list polar bears as a threatened species under the United States Endangered Species Act. To provide information for the Department of the Interior...

Forecasting residential electricity demand in provincial China.

PubMed

Liao, Hua; Liu, Yanan; Gao, Yixuan; Hao, Yu; Ma, Xiao-Wei; Wang, Kan

2017-03-01

In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016-2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016-2020, and populous provinces such as Guangdong will be the main contributors to the increments.

Applying Thiessen Polygon Catchment Areas and Gridded Population Weights to Estimate Conflict-Driven Population Changes in South Sudan

NASA Astrophysics Data System (ADS)

Jordan, L.

2017-10-01

Recent violence in South Sudan produced significant levels of conflict-driven migration undermining the accuracy and utility of both national and local level population forecasts commonly used in demographic estimates, public health metrics and food security proxies. This article explores the use of Thiessen Polygons and population grids (Gridded Population of the World, WorldPop and LandScan) as weights for estimating the catchment areas for settlement locations that serve large populations of internally displaced persons (IDP), in order to estimate the county-level in- and out-migration attributable to conflict-driven displacement between 2014-2015. Acknowledging IDP totals improves internal population estimates presented by global population databases. Unlike other forecasts, which produce spatially uniform increases in population, accounting for displaced population reveals that 15 percent of counties (n = 12) increased in population over 20 percent, and 30 percent of counties (n = 24) experienced zero or declining population growth, due to internal displacement and refugee out-migration. Adopting Thiessen Polygon catchment zones for internal migration estimation can be applied to other areas with United Nations IDP settlement data, such as Yemen, Somalia, and Nigeria.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2015-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2014-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Factors that Influence the Use of Climate Forecasts: Evidence from the 1997/98 El Niño Event in Peru.

NASA Astrophysics Data System (ADS)

Orlove, Benjamin S.; Broad, Kenneth; Petty, Aaron M.

2004-11-01

This article analyzes the use of climate forecasts among members of the Peruvian fishing sector during the 1997/98 El Niño event. It focuses on the effect of the time of hearing a forecast on the socioeconomic responses to the forecast. Findings are based on data collected from a survey of 596 persons in five ports spanning the length of the Peruvian coast. Respondents include commercial and artisanal fishers, plant workers, managers, and firm owners.These data fill an important gap in the literature on the use of forecasts. Though modelers have discussed the effects of the timing of the dissemination and reception of forecasts, along with other factors, on acting on a forecast once it has been heard, few researchers have gathered empirical evidence on these topics.The 1997/98 El Niño event was covered extensively by the media throughout Peru, affording the opportunity to study the effect of hearing forecasts on actions taken by members of a population directly impacted by ENSO events. Findings of this study examine the relationships among 1) socioeconomic variables, including geographic factors, age, education, income level, organizational ties, and media access; 2) time of hearing the forecast; and 3) actions taken in response to the forecast. Socioeconomic variables have a strong effect on the time of hearing the forecast and the actions taken in response to the forecast; however, time of hearing does not have an independent effect on taking action. The article discusses the implications of these findings for the application of forecasts.A supplement to this article is available online (dx.doi.org/10.1175/BAMS-85-11-Orlove)

Forecasting human exposure to atmospheric pollutants in Portugal - A modelling approach

NASA Astrophysics Data System (ADS)

Borrego, C.; Sá, E.; Monteiro, A.; Ferreira, J.; Miranda, A. I.

2009-12-01

Air pollution has become one main environmental concern because of its known impact on human health. Aiming to inform the population about the air they are breathing, several air quality modelling systems have been developed and tested allowing the assessment and forecast of air pollution ambient levels in many countries. However, every day, an individual is exposed to different concentrations of atmospheric pollutants as he/she moves from and to different outdoor and indoor places (the so-called microenvironments). Therefore, a more efficient way to prevent the population from the health risks caused by air pollution should be based on exposure rather than air concentrations estimations. The objective of the present study is to develop a methodology to forecast the human exposure of the Portuguese population based on the air quality forecasting system available and validated for Portugal since 2005. Besides that, a long-term evaluation of human exposure estimates aims to be obtained using one-year of this forecasting system application. Additionally, a hypothetical 50% emission reduction scenario has been designed and studied as a contribution to study emission reduction strategies impact on human exposure. To estimate the population exposure the forecasting results of the air quality modelling system MM5-CHIMERE have been combined with the population spatial distribution over Portugal and their time-activity patterns, i.e. the fraction of the day time spent in specific indoor and outdoor places. The population characterization concerning age, work, type of occupation and related time spent was obtained from national census and available enquiries performed by the National Institute of Statistics. A daily exposure estimation module has been developed gathering all these data and considering empirical indoor/outdoor relations from literature to calculate the indoor concentrations in each one of the microenvironments considered, namely home, office/school, and other indoors (leisure activities like shopping areas, gym, theatre/cinema and restaurants). The results show how this developed modelling system can be useful to anticipate air pollution episodes and to estimate their effects on human health on a long-term basis. The two metropolitan areas of Porto and Lisbon are identified as the most critical ones in terms of air pollution effects on human health over Portugal in a long-term as well as in a short-term perspective. The coexistence of high concentration values and high population density is the key factor for these stressed areas. Regarding the 50% emission reduction scenario, the model results are significantly different for both pollutants: there is a small overall reduction in the individual exposure values of PM 10 (<10 μg m -3 h), but for O 3, in contrast, there is an extended area where exposure values increase with emission reduction. This detailed knowledge is a prerequisite for the development of effective policies to reduce the foreseen adverse impact of air pollution on human health and to act on time.

A new approach on seismic mortality estimations based on average population density

NASA Astrophysics Data System (ADS)

Zhu, Xiaoxin; Sun, Baiqing; Jin, Zhanyong

2016-12-01

This study examines a new methodology to predict the final seismic mortality from earthquakes in China. Most studies established the association between mortality estimation and seismic intensity without considering the population density. In China, however, the data are not always available, especially when it comes to the very urgent relief situation in the disaster. And the population density varies greatly from region to region. This motivates the development of empirical models that use historical death data to provide the path to analyze the death tolls for earthquakes. The present paper employs the average population density to predict the final death tolls in earthquakes using a case-based reasoning model from realistic perspective. To validate the forecasting results, historical data from 18 large-scale earthquakes occurred in China are used to estimate the seismic morality of each case. And a typical earthquake case occurred in the northwest of Sichuan Province is employed to demonstrate the estimation of final death toll. The strength of this paper is that it provides scientific methods with overall forecast errors lower than 20 %, and opens the door for conducting final death forecasts with a qualitative and quantitative approach. Limitations and future research are also analyzed and discussed in the conclusion.

Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan.

PubMed

Chen, Brian K; Jalal, Hawre; Hashimoto, Hideki; Suen, Sze-Chuan; Eggleston, Karen; Hurley, Michael; Schoemaker, Lena; Bhattacharya, Jay

2016-12-01

Japan has experienced pronounced population aging, and now has the highest proportion of elderly adults in the world. Yet few projections of Japan's future demography go beyond estimating population by age and sex to forecast the complex evolution of the health and functioning of the future elderly. This study estimates a new state-transition microsimulation model - the Japanese Future Elderly Model (FEM) - for Japan. We use the model to forecast disability and health for Japan's future elderly. Our simulation suggests that by 2040, over 27 percent of Japan's elderly will exhibit 3 or more limitations in IADLs and social functioning; almost one in 4 will experience difficulties with 3 or more ADLs; and approximately one in 5 will suffer limitations in cognitive or intellectual functioning. Since the majority of the increase in disability arises from the aging of the Japanese population, prevention efforts that reduce age-specific morbidity can help reduce the burden of disability but may have only a limited impact on reducing the overall prevalence of disability among Japanese elderly. While both age and morbidity contribute to a predicted increase in disability burden among elderly Japanese in the future, our simulation results suggest that the impact of population aging exceeds the effect of age-specific morbidity on increasing disability in Japan's future.

Integrating bioclimate with population models to improve forecasts of species extinctions under climate change.

PubMed

Brook, Barry W; Akçakaya, H Resit; Keith, David A; Mace, Georgina M; Pearson, Richard G; Araújo, Miguel B

2009-12-23

Climate change is already affecting species worldwide, yet existing methods of risk assessment have not considered interactions between demography and climate and their simultaneous effect on habitat distribution and population viability. To address this issue, an international workshop was held at the University of Adelaide in Australia, 25-29 May 2009, bringing leading species distribution and population modellers together with plant ecologists. Building on two previous workshops in the UK and Spain, the participants aimed to develop methodological standards and case studies for integrating bioclimatic and metapopulation models, to provide more realistic forecasts of population change, habitat fragmentation and extinction risk under climate change. The discussions and case studies focused on several challenges, including spatial and temporal scale contingencies, choice of predictive climate, land use, soil type and topographic variables, procedures for ensemble forecasting of both global climate and bioclimate models and developing demographic structures that are realistic and species-specific and yet allow generalizations of traits that make species vulnerable to climate change. The goal is to provide general guidelines for assessing the Red-List status of large numbers of species potentially at risk, owing to the interactions of climate change with other threats such as habitat destruction, overexploitation and invasive species.

Methane and nitrous oxide emissions from livestock agriculture in 16 local administrative districts of Korea.

PubMed

Ji, Eun Sook; Park, Kyu-Hyun

2012-12-01

This study was conducted to evaluate methane (CH4) and nitrous oxide (N2O) emissions from livestock agriculture in 16 local administrative districts of Korea from 1990 to 2030. National Inventory Report used 3 yr averaged livestock population but this study used 1 yr livestock population to find yearly emission fluctuations. Extrapolation of the livestock population from 1990 to 2009 was used to forecast future livestock population from 2010 to 2030. Past (yr 1990 to 2009) and forecasted (yr 2010 to 2030) averaged enteric CH4 emissions and CH4 and N2O emissions from manure treatment were estimated. In the section of enteric fermentation, forecasted average CH4 emissions from 16 local administrative districts were estimated to increase by 4%-114% compared to that of the past except for Daejeon (-63%), Seoul (-36%) and Gyeonggi (-7%). As for manure treatment, forecasted average CH4 emissions from the 16 local administrative districts were estimated to increase by 3%-124% compared to past average except for Daejeon (-77%), Busan (-60%), Gwangju (-48%) and Seoul (-8%). For manure treatment, forecasted average N2O emissions from the 16 local administrative districts were estimated to increase by 10%-153% compared to past average CH4 emissions except for Daejeon (-60%), Seoul (-4.0%), and Gwangju (-0.2%). With the carbon dioxide equivalent emissions (CO2-Eq), forecasted average CO2-Eq from the 16 local administrative districts were estimated to increase by 31%-120% compared to past average CH4 emissions except Daejeon (-65%), Seoul (-24%), Busan (-18%), Gwangju (-8%) and Gyeonggi (-1%). The decreased CO2-Eq from 5 local administrative districts was only 34 kt, which was insignificantly small compared to increase of 2,809 kt from other 11 local administrative districts. Annual growth rates of enteric CH4 emissions, CH4 and N2O emissions from manure management in Korea from 1990 to 2009 were 1.7%, 2.6%, and 3.2%, respectively. The annual growth rate of total CO2-Eq was 2.2%. Efforts by the local administrative offices to improve the accuracy of activity data are essential to improve GHG inventories. Direct measurements of GHG emissions from enteric fermentation and manure treatment systems will further enhance the accuracy of the GHG data. (Key Words: Greenhouse Gas, Methane, Nitrous Oxide, Carbon Dioxide Equivalent Emission, Climate Change).

Forecasting outbreaks of the Douglas-fir tussock moth from lower crown cocoon samples.

Treesearch

Richard R. Mason; Donald W. Scott; H. Gene Paul

1993-01-01

A predictive technique using a simple linear regression was developed to forecast the midcrown density of small tussock moth larvae from estimates of cocoon density in the previous generation. The regression estimator was derived from field samples of cocoons and larvae taken from a wide range of nonoutbreak tussock moth populations. The accuracy of the predictions was...

Predicting Rehabilitation Success Rate Trends among Ethnic Minorities Served by State Vocational Rehabilitation Agencies: A National Time Series Forecast Model Demonstration Study

ERIC Educational Resources Information Center

Moore, Corey L.; Wang, Ningning; Washington, Janique Tynez

2017-01-01

Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts…

Climate science and famine early warning

USGS Publications Warehouse

Verdin, James P.; Funk, Chris; Senay, Gabriel B.; Choularton, R.

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning.

PubMed

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-11-29

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning

PubMed Central

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101

Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City.

PubMed

Yang, Wan; Olson, Donald R; Shaman, Jeffrey

2016-11-01

The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure; however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast.

Blending forest fire smoke forecasts with observed data can improve their utility for public health applications

NASA Astrophysics Data System (ADS)

Yuchi, Weiran; Yao, Jiayun; McLean, Kathleen E.; Stull, Roland; Pavlovic, Radenko; Davignon, Didier; Moran, Michael D.; Henderson, Sarah B.

2016-11-01

Fine particulate matter (PM2.5) generated by forest fires has been associated with a wide range of adverse health outcomes, including exacerbation of respiratory diseases and increased risk of mortality. Due to the unpredictable nature of forest fires, it is challenging for public health authorities to reliably evaluate the magnitude and duration of potential exposures before they occur. Smoke forecasting tools are a promising development from the public health perspective, but their widespread adoption is limited by their inherent uncertainties. Observed measurements from air quality monitoring networks and remote sensing platforms are more reliable, but they are inherently retrospective. It would be ideal to reduce the uncertainty in smoke forecasts by integrating any available observations. This study takes spatially resolved PM2.5 estimates from an empirical model that integrates air quality measurements with satellite data, and averages them with PM2.5 predictions from two smoke forecasting systems. Two different indicators of population respiratory health are then used to evaluate whether the blending improved the utility of the smoke forecasts. Among a total of six models, including two single forecasts and four blended forecasts, the blended estimates always performed better than the forecast values alone. Integrating measured observations into smoke forecasts could improve public health preparedness for smoke events, which are becoming more frequent and intense as the climate changes.

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States.

PubMed

Liu, Yan; Watson, Stella C; Gettings, Jenna R; Lund, Robert B; Nordone, Shila K; Yabsley, Michael J; McMahan, Christopher S

2017-01-01

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast's construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011-2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year's regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011-2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases.

Flood Forecasting in Wales: Challenges and Solutions

NASA Astrophysics Data System (ADS)

How, Andrew; Williams, Christopher

2015-04-01

With steep, fast-responding river catchments, exposed coastal reaches with large tidal ranges and large population densities in some of the most at-risk areas; flood forecasting in Wales presents many varied challenges. Utilising advances in computing power and learning from best practice within the United Kingdom and abroad have seen significant improvements in recent years - however, many challenges still remain. Developments in computing and increased processing power comes with a significant price tag; greater numbers of data sources and ensemble feeds brings a better understanding of uncertainty but the wealth of data needs careful management to ensure a clear message of risk is disseminated; new modelling techniques utilise better and faster computation, but lack the history of record and experience gained from the continued use of more established forecasting models. As a flood forecasting team we work to develop coastal and fluvial forecasting models, set them up for operational use and manage the duty role that runs the models in real time. An overview of our current operational flood forecasting system will be presented, along with a discussion on some of the solutions we have in place to address the challenges we face. These include: • real-time updating of fluvial models • rainfall forecasting verification • ensemble forecast data • longer range forecast data • contingency models • offshore to nearshore wave transformation • calculation of wave overtopping

An experimental system for flood risk forecasting and monitoring at global scale

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Alfieri, Lorenzo; Kalas, Milan; Lorini, Valerio; Salamon, Peter

2017-04-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by a wide range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasting, combining streamflow estimations with expected inundated areas and flood impacts. Finally, emerging technologies such as crowdsourcing and social media monitoring can play a crucial role in flood disaster management and preparedness. Here, we present some recent advances of an experimental procedure for near-real time flood mapping and impact assessment. The procedure translates in near real-time the daily streamflow forecasts issued by the Global Flood Awareness System (GloFAS) into event-based flood hazard maps, which are then combined with exposure and vulnerability information at global scale to derive risk forecast. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To increase the reliability of our forecasts we propose the integration of model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification and correction of impact forecasts. Finally, we present the results of preliminary tests which show the potential of the proposed procedure in supporting emergency response and management.

Forecasting Japanese encephalitis incidence from historical morbidity patterns: Statistical analysis with 27 years of observation in Assam, India.

PubMed

Handique, Bijoy K; Khan, Siraj A; Mahanta, J; Sudhakar, S

2014-09-01

Japanese encephalitis (JE) is one of the dreaded mosquito-borne viral diseases mostly prevalent in south Asian countries including India. Early warning of the disease in terms of disease intensity is crucial for taking adequate and appropriate intervention measures. The present study was carried out in Dibrugarh district in the state of Assam located in the northeastern region of India to assess the accuracy of selected forecasting methods based on historical morbidity patterns of JE incidence during the past 22 years (1985-2006). Four selected forecasting methods, viz. seasonal average (SA), seasonal adjustment with last three observations (SAT), modified method adjusting long-term and cyclic trend (MSAT), and autoregressive integrated moving average (ARIMA) have been employed to assess the accuracy of each of the forecasting methods. The forecasting methods were validated for five consecutive years from 2007-2012 and accuracy of each method has been assessed. The forecasting method utilising seasonal adjustment with long-term and cyclic trend emerged as best forecasting method among the four selected forecasting methods and outperformed the even statistically more advanced ARIMA method. Peak of the disease incidence could effectively be predicted with all the methods, but there are significant variations in magnitude of forecast errors among the selected methods. As expected, variation in forecasts at primary health centre (PHC) level is wide as compared to that of district level forecasts. The study showed that adopted forecasting techniques could reasonably forecast the intensity of JE cases at PHC level without considering the external variables. The results indicate that the understanding of long-term and cyclic trend of the disease intensity will improve the accuracy of the forecasts, but there is a need for making the forecast models more robust to explain sudden variation in the disease intensity with detail analysis of parasite and host population dynamics.

Forecasting Cause-Specific Mortality in Korea up to Year 2032.

PubMed

Yun, Jae-Won; Son, Mia

2016-08-01

Forecasting cause-specific mortality can help estimate the future burden of diseases and provide a clue for preventing diseases. Our objective was to forecast the mortality for causes of death in the future (2013-2032) based on the past trends (1983-2012) in Korea. The death data consisted of 12 major causes of death from 1983 to 2012 and the population data consisted of the observed and estimated populations (1983-2032) in Korea. The modified age-period-cohort model with an R-based program, nordpred software, was used to forecast future mortality. Although the age-standardized rates for the world standard population for both sexes are expected to decrease from 2008-2012 to 2028-2032 (males: -31.4%, females: -32.3%), the crude rates are expected to increase (males: 46.3%, females: 33.4%). The total number of deaths is also estimated to increase (males: 52.7%, females: 41.9%). Additionally, the largest contribution to the overall change in deaths was the change in the age structures. Several causes of death are projected to increase in both sexes (cancer, suicide, heart diseases, pneumonia and Alzheimer's disease), while others are projected to decrease (cerebrovascular diseases, liver diseases, diabetes mellitus, traffic accidents, chronic lower respiratory diseases, and pulmonary tuberculosis). Cancer is expected to be the highest cause of death for both the 2008-2012 and 2028-2032 time periods in Korea. To reduce the disease burden, projections of the future cause-specific mortality should be used as fundamental data for developing public health policies.

A scoping review of malaria forecasting: past work and future directions

PubMed Central

Zinszer, Kate; Verma, Aman D; Charland, Katia; Brewer, Timothy F; Brownstein, John S; Sun, Zhuoyu; Buckeridge, David L

2012-01-01

Objectives There is a growing body of literature on malaria forecasting methods and the objective of our review is to identify and assess methods, including predictors, used to forecast malaria. Design Scoping review. Two independent reviewers searched information sources, assessed studies for inclusion and extracted data from each study. Information sources Search strategies were developed and the following databases were searched: CAB Abstracts, EMBASE, Global Health, MEDLINE, ProQuest Dissertations & Theses and Web of Science. Key journals and websites were also manually searched. Eligibility criteria for included studies We included studies that forecasted incidence, prevalence or epidemics of malaria over time. A description of the forecasting model and an assessment of the forecast accuracy of the model were requirements for inclusion. Studies were restricted to human populations and to autochthonous transmission settings. Results We identified 29 different studies that met our inclusion criteria for this review. The forecasting approaches included statistical modelling, mathematical modelling and machine learning methods. Climate-related predictors were used consistently in forecasting models, with the most common predictors being rainfall, relative humidity, temperature and the normalised difference vegetation index. Model evaluation was typically based on a reserved portion of data and accuracy was measured in a variety of ways including mean-squared error and correlation coefficients. We could not compare the forecast accuracy of models from the different studies as the evaluation measures differed across the studies. Conclusions Applying different forecasting methods to the same data, exploring the predictive ability of non-environmental variables, including transmission reducing interventions and using common forecast accuracy measures will allow malaria researchers to compare and improve models and methods, which should improve the quality of malaria forecasting. PMID:23180505

Agent-Based Model Forecasts Aging of the Population of People Who Inject Drugs in Metropolitan Chicago and Changing Prevalence of Hepatitis C Infections.

PubMed

Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E

2015-01-01

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010-2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(± 2)% to 36(± 5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(± 5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(± 1) to 40(± 2) with a corresponding increase from 59(± 2)% to 80(± 6)% in the proportion of the population >30 years old. Our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.

Forecasting outdoor recreation participation: a cohort-component projection model for Illinois residents

Treesearch

John F. Dwyer

1995-01-01

Population projections for Illinois predicts lower growth, an older population, and increased racial diversity. If percent of the population participating in outdoor recreation activities by age and race remains at present levels, cohort-component projection models suggest that with projected changes in the population between 1990 and 2025, the number of Illinois...

An Analysis on the Unemployment Rate in the Philippines: A Time Series Data Approach

NASA Astrophysics Data System (ADS)

Urrutia, J. D.; Tampis, R. L.; E Atienza, JB

2017-03-01

This study aims to formulate a mathematical model for forecasting and estimating unemployment rate in the Philippines. Also, factors which can predict the unemployment is to be determined among the considered variables namely Labor Force Rate, Population, Inflation Rate, Gross Domestic Product, and Gross National Income. Granger-causal relationship and integration among the dependent and independent variables are also examined using Pairwise Granger-causality test and Johansen Cointegration Test. The data used were acquired from the Philippine Statistics Authority, National Statistics Office, and Bangko Sentral ng Pilipinas. Following the Box-Jenkins method, the formulated model for forecasting the unemployment rate is SARIMA (6, 1, 5) × (0, 1, 1)4 with a coefficient of determination of 0.79. The actual values are 99 percent identical to the predicted values obtained through the model, and are 72 percent closely relative to the forecasted ones. According to the results of the regression analysis, Labor Force Rate and Population are the significant factors of unemployment rate. Among the independent variables, Population, GDP, and GNI showed to have a granger-causal relationship with unemployment. It is also found that there are at least four cointegrating relations between the dependent and independent variables.

Sources and Sinks: Elucidating Mechanisms, Documenting Patterns, and Forecasting Impacts

DTIC Science & Technology

2017-01-18

Molecular Ecology 17: 3628-3639. Fazio III, V. W., Miles, D. B., & White, M. M. 2004. Genetic differentiation in the endangered Black-capped Vireo...exploration of accuracy and power. Molecular Ecology 13: 55–65. Raymond, M., & Rousset, F. 1995. GENEPOP (version 1.2): population genetics software for...SUPPLEMENTAL GENETICS MEMO Sources and Sinks: Elucidating Mechanisms, Documenting Patterns, and Forecasting Impacts SERDP Project RC-2120

Forecasting the Range-wide Status of Polar Bears at Selected Times in the 21st Century

Treesearch

Steven C. Amstrup; Bruce G. Marcot; David C. Douglas

2007-01-01

To inform the U.S. Fish and Wildlife Service decision whether or not to list polar bears as threatened under the Endangered Species Act (ESA), we forecast the status of the world's polar bear (Ursus maritimus) populations 45, 75 and 100 years into the future. We applied the best available information about predicted changes in sea ice in the...

A forecast of typhoid conjugate vaccine introduction and demand in typhoid endemic low- and middle-income countries to support vaccine introduction policy and decisions.

PubMed

Mogasale, Vittal; Ramani, Enusa; Park, Il Yeon; Lee, Jung Seok

2017-09-02

A Typhoid Conjugate Vaccine (TCV) is expected to acquire WHO prequalification soon, which will pave the way for its use in many low- and middle-income countries where typhoid fever is endemic. Thus it is critical to forecast future vaccine demand to ensure supply meets demand, and to facilitate vaccine policy and introduction planning. We forecasted introduction dates for countries based on specific criteria and estimated vaccine demand by year for defined vaccination strategies in 2 scenarios: rapid vaccine introduction and slow vaccine introduction. In the rapid introduction scenario, we forecasted 17 countries and India introducing TCV in the first 5 y of the vaccine's availability while in the slow introduction scenario we forecasted 4 countries and India introducing TCV in the same time period. If the vaccine is targeting infants in high-risk populations as a routine single dose, the vaccine demand peaks around 40 million doses per year under the rapid introduction scenario. Similarly, if the vaccine is targeting infants in the general population as a routine single dose, the vaccine demand increases to 160 million doses per year under the rapid introduction scenario. The demand forecast projected here is an upper bound estimate of vaccine demand, where actual demand depends on various factors such as country priorities, actual vaccine introduction, vaccination strategies, Gavi financing, costs, and overall product profile. Considering the potential role of TCV in typhoid control globally; manufacturers, policymakers, donors and financing bodies should work together to ensure vaccine access through sufficient production capacity, early WHO prequalification of the vaccine, continued Gavi financing and supportive policy.

A forecast of typhoid conjugate vaccine introduction and demand in typhoid endemic low- and middle-income countries to support vaccine introduction policy and decisions

PubMed Central

Ramani, Enusa; Park, Il Yeon; Lee, Jung Seok

2017-01-01

ABSTRACT A Typhoid Conjugate Vaccine (TCV) is expected to acquire WHO prequalification soon, which will pave the way for its use in many low- and middle-income countries where typhoid fever is endemic. Thus it is critical to forecast future vaccine demand to ensure supply meets demand, and to facilitate vaccine policy and introduction planning. We forecasted introduction dates for countries based on specific criteria and estimated vaccine demand by year for defined vaccination strategies in 2 scenarios: rapid vaccine introduction and slow vaccine introduction. In the rapid introduction scenario, we forecasted 17 countries and India introducing TCV in the first 5 y of the vaccine's availability while in the slow introduction scenario we forecasted 4 countries and India introducing TCV in the same time period. If the vaccine is targeting infants in high-risk populations as a routine single dose, the vaccine demand peaks around 40 million doses per year under the rapid introduction scenario. Similarly, if the vaccine is targeting infants in the general population as a routine single dose, the vaccine demand increases to 160 million doses per year under the rapid introduction scenario. The demand forecast projected here is an upper bound estimate of vaccine demand, where actual demand depends on various factors such as country priorities, actual vaccine introduction, vaccination strategies, Gavi financing, costs, and overall product profile. Considering the potential role of TCV in typhoid control globally; manufacturers, policymakers, donors and financing bodies should work together to ensure vaccine access through sufficient production capacity, early WHO prequalification of the vaccine, continued Gavi financing and supportive policy. PMID:28604164

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carson, K.S.

The presence of overpopulation or unsustainable population growth may place pressure on the food and water supplies of countries in sensitive areas of the world. Severe air or water pollution may place additional pressure on these resources. These pressures may generate both internal and international conflict in these areas as nations struggle to provide for their citizens. Such conflicts may result in United States intervention, either unilaterally, or through the United Nations. Therefore, it is in the interests of the United States to identify potential areas of conflict in order to properly train and allocate forces. The purpose of thismore » research is to forecast the probability of conflict in a nation as a function of it s environmental conditions. Probit, logit and ordered probit models are employed to forecast the probability of a given level of conflict. Data from 95 countries are used to estimate the models. Probability forecasts are generated for these 95 nations. Out-of sample forecasts are generated for an additional 22 nations. These probabilities are then used to rank nations from highest probability of conflict to lowest. The results indicate that the dependence of a nation`s economy on agriculture, the rate of deforestation, and the population density are important variables in forecasting the probability and level of conflict. These results indicate that environmental variables do play a role in generating or exacerbating conflict. It is unclear that the United States military has any direct role in mitigating the environmental conditions that may generate conflict. A more important role for the military is to aid in data gathering to generate better forecasts so that the troops are adequntely prepared when conflicts arises.« less

Challenges and Opportunities in Disease Forecasting in Outbreak Settings: A Case Study of Measles in Lola Prefecture, Guinea

PubMed Central

Graham, Matthew; Suk, Jonathan E.; Takahashi, Saki; Metcalf, C. Jessica; Jimenez, A. Paez; Prikazsky, Vladimir; Ferrari, Matthew J.; Lessler, Justin

2018-01-01

Abstract. We report on and evaluate the process and findings of a real-time modeling exercise in response to an outbreak of measles in Lola prefecture, Guinea, in early 2015 in the wake of the Ebola crisis. Multiple statistical methods for the estimation of the size of the susceptible (i.e., unvaccinated) population were applied to weekly reported measles case data on seven subprefectures throughout Lola. Stochastic compartmental models were used to project future measles incidence in each subprefecture in both an initial and a follow-up iteration of forecasting. Measles susceptibility among 1- to 5-year-olds was estimated to be between 24% and 43% at the beginning of the outbreak. Based on this high baseline susceptibility, initial projections forecasted a large outbreak occurring over approximately 10 weeks and infecting 40 children per 1,000. Subsequent forecasts based on updated data mitigated this initial projection, but still predicted a significant outbreak. A catch-up vaccination campaign took place at the same time as this second forecast and measles cases quickly receded. Of note, case reports used to fit models changed significantly between forecast rounds. Model-based projections of both current population risk and future incidence can help in setting priorities and planning during an outbreak response. A swiftly changing situation on the ground, coupled with data uncertainties and the need to adjust standard analytical approaches to deal with sparse data, presents significant challenges. Appropriate presentation of results as planning scenarios, as well as presentations of uncertainty and two-way communication, is essential to the effective use of modeling studies in outbreak response. PMID:29532773

Challenges and Opportunities in Disease Forecasting in Outbreak Settings: A Case Study of Measles in Lola Prefecture, Guinea.

PubMed

Graham, Matthew; Suk, Jonathan E; Takahashi, Saki; Metcalf, C Jessica; Jimenez, A Paez; Prikazsky, Vladimir; Ferrari, Matthew J; Lessler, Justin

2018-05-01

We report on and evaluate the process and findings of a real-time modeling exercise in response to an outbreak of measles in Lola prefecture, Guinea, in early 2015 in the wake of the Ebola crisis. Multiple statistical methods for the estimation of the size of the susceptible (i.e., unvaccinated) population were applied to weekly reported measles case data on seven subprefectures throughout Lola. Stochastic compartmental models were used to project future measles incidence in each subprefecture in both an initial and a follow-up iteration of forecasting. Measles susceptibility among 1- to 5-year-olds was estimated to be between 24% and 43% at the beginning of the outbreak. Based on this high baseline susceptibility, initial projections forecasted a large outbreak occurring over approximately 10 weeks and infecting 40 children per 1,000. Subsequent forecasts based on updated data mitigated this initial projection, but still predicted a significant outbreak. A catch-up vaccination campaign took place at the same time as this second forecast and measles cases quickly receded. Of note, case reports used to fit models changed significantly between forecast rounds. Model-based projections of both current population risk and future incidence can help in setting priorities and planning during an outbreak response. A swiftly changing situation on the ground, coupled with data uncertainties and the need to adjust standard analytical approaches to deal with sparse data, presents significant challenges. Appropriate presentation of results as planning scenarios, as well as presentations of uncertainty and two-way communication, is essential to the effective use of modeling studies in outbreak response.

An operational procedure for rapid flood risk assessment in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Salamon, Peter; Bianchi, Alessandra; Alfieri, Lorenzo; Feyen, Luc

2017-07-01

The development of methods for rapid flood mapping and risk assessment is a key step to increase the usefulness of flood early warning systems and is crucial for effective emergency response and flood impact mitigation. Currently, flood early warning systems rarely include real-time components to assess potential impacts generated by forecasted flood events. To overcome this limitation, this study describes the benchmarking of an operational procedure for rapid flood risk assessment based on predictions issued by the European Flood Awareness System (EFAS). Daily streamflow forecasts produced for major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in terms of flood-prone areas, economic damage and affected population, infrastructures and cities.An extensive testing of the operational procedure has been carried out by analysing the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-based and report-based flood extent data, while modelled estimates of economic damage and affected population are compared against ground-based estimations. Finally, we evaluate the skill of risk estimates derived from EFAS flood forecasts with different lead times and combinations of probabilistic forecasts. Results highlight the potential of the real-time operational procedure in helping emergency response and management.

Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; Funk, Christopher C.; Hoell, Andrew

2014-01-01

In this study we implement and evaluate a simple 'hybrid' forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble's (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The 'hybrid approach' described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.

Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

NASA Astrophysics Data System (ADS)

Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

2017-02-01

Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City

PubMed Central

2016-01-01

The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure; however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast. PMID:27855155

Forecasting the impact of transport improvements on commuting and residential choice

NASA Astrophysics Data System (ADS)

Elhorst, J. Paul; Oosterhaven, Jan

2006-03-01

This paper develops a probabilistic, competing-destinations, assignment model that predicts changes in the spatial pattern of the working population as a result of transport improvements. The choice of residence is explained by a new non-parametric model, which represents an alternative to the popular multinominal logit model. Travel times between zones are approximated by a normal distribution function with different mean and variance for each pair of zones, whereas previous models only use average travel times. The model’s forecast error of the spatial distribution of the Dutch working population is 7% when tested on 1998 base-year data. To incorporate endogenous changes in its causal variables, an almost ideal demand system is estimated to explain the choice of transport mode, and a new economic geography inter-industry model (RAEM) is estimated to explain the spatial distribution of employment. In the application, the model is used to forecast the impact of six mutually exclusive Dutch core-periphery railway proposals in the projection year 2020.

In Brief: Forecasting meningitis threats

NASA Astrophysics Data System (ADS)

Showstack, Randy

2008-12-01

The University Corporation for Atmospheric Research (UCAR), in conjunction with a team of health and weather organizations, has launched a project to provide weather forecasts to medical officials in Africa to help reduce outbreaks of meningitis. The forecasts will enable local health care providers to target vaccination programs more effectively. In 2009, meteorologists with the National Center for Atmospheric Research, which is managed by UCAR, will begin issuing 14-day forecasts of atmospheric conditions in Ghana. Later, UCAR plans to work closely with health experts from several African countries to design and test a decision support system to provide health officials with useful meteorological information. ``By targeting forecasts in regions where meningitis is a threat, we may be able to help vulnerable populations. Ultimately, we hope to build on this project and provide information to public health programs battling weather-related diseases in other parts of the world,'' said Rajul Pandya, director of UCAR's Community Building Program. Funding for the project comes from a $900,000 grant from Google.org, the philanthropic arm of the Internet search company.

Increased Accuracy in Statistical Seasonal Hurricane Forecasting

NASA Astrophysics Data System (ADS)

Nateghi, R.; Quiring, S. M.; Guikema, S. D.

2012-12-01

Hurricanes are among the costliest and most destructive natural hazards in the U.S. Accurate hurricane forecasts are crucial to optimal preparedness and mitigation decisions in the U.S. where 50 percent of the population lives within 50 miles of the coast. We developed a flexible statistical approach to forecast annual number of hurricanes in the Atlantic region during the hurricane season. Our model is based on the method of Random Forest and captures the complex relationship between hurricane activity and climatic conditions through careful variable selection, model testing and validation. We used the National Hurricane Center's Best Track hurricane data from 1949-2011 and sixty-one candidate climate descriptors to develop our model. The model includes information prior to the hurricane season, i.e., from the last three months of the previous year (Oct. through Dec.) and the first five months of the current year (January through May). Our forecast errors are substantially lower than other leading forecasts such as that of the National Oceanic and Atmospheric Administration (NOAA).

Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

PubMed Central

Lewnard, Joseph A.; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R.; Glesby, Marshall J.; Ko, Albert I.; Carvalho, Edgar M.; Schriefer, Albert; Weinberger, Daniel M.

2014-01-01

Introduction Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. PMID:25356734

Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

PubMed

Lewnard, Joseph A; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R; Glesby, Marshall J; Ko, Albert I; Carvalho, Edgar M; Schriefer, Albert; Weinberger, Daniel M

2014-10-01

Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan

PubMed Central

Chen, Brian K.; Jalal, Hawre; Hashimoto, Hideki; Suen, Sze-chuan; Eggleston, Karen; Hurley, Michael; Schoemaker, Lena; Bhattacharya, Jay

2016-01-01

Japan has experienced pronounced population aging, and now has the highest proportion of elderly adults in the world. Yet few projections of Japan’s future demography go beyond estimating population by age and sex to forecast the complex evolution of the health and functioning of the future elderly. This study estimates a new state-transition microsimulation model – the Japanese Future Elderly Model (FEM) – for Japan. We use the model to forecast disability and health for Japan’s future elderly. Our simulation suggests that by 2040, over 27 percent of Japan’s elderly will exhibit 3 or more limitations in IADLs and social functioning; almost one in 4 will experience difficulties with 3 or more ADLs; and approximately one in 5 will suffer limitations in cognitive or intellectual functioning. Since the majority of the increase in disability arises from the aging of the Japanese population, prevention efforts that reduce age-specific morbidity can help reduce the burden of disability but may have only a limited impact on reducing the overall prevalence of disability among Japanese elderly. While both age and morbidity contribute to a predicted increase in disability burden among elderly Japanese in the future, our simulation results suggest that the impact of population aging exceeds the effect of age-specific morbidity on increasing disability in Japan’s future. PMID:28580275

An experimental system for flood risk forecasting at global scale

NASA Astrophysics Data System (ADS)

Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

2016-12-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States

PubMed Central

Liu, Yan; Watson, Stella C.; Gettings, Jenna R.; Lund, Robert B.; Nordone, Shila K.; McMahan, Christopher S.

2017-01-01

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011–2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases. PMID:28738085

Climate and Salmon Restoration in the Columbia River Basin: The Role and Usability of Seasonal Forecasts.

NASA Astrophysics Data System (ADS)

Pulwarty, Roger S.; Redmond, Kelly T.

1997-03-01

The Pacific Northwest is dependent on the vast and complex Columbia River system for power production, irrigation, navigation, flood control, recreation, municipal and industrial water supplies, and fish and wildlife habitat. In recent years Pacific salmon populations in this region, a highly valued cultural and economic resource, have declined precipitously. Since 1980, regional entities have embarked on the largest effort at ecosystem management undertaken to date in the United States, primarily aimed at balancing hydropower demands with salmon restoration activities. It has become increasingly clear that climatically driven fluctuations in the freshwater and marine environments occupied by these fish are an important influence on population variability. It is also clear that there are significant prospects of climate predictability that may prove advantageous in managing the water resources shared by the long cast of regional interests. The main thrusts of this study are 1) to describe the climate and management environments of the Columbia River basin, 2) to assess the present degree of use and benefits of available climate information, 3) to identify new roles and applications made possible by recent advances in climate forecasting, and 4) to understand, from the point of view of present and potential users in specific contexts of salmon management, what information might be needed, for what uses, and when, where, and how it should be provided. Interviews were carried out with 32 individuals in 19 organizations involved in salmon management decisions. Primary needs were in forecasting runoff volume and timing, river transit times, and stream temperatures, as much as a year or more in advance. Most respondents desired an accuracy of 75% for a seasonal forecast. Despite the significant influence of precipitation and its subsequent hydrologic impacts on the regional economy, no specific use of the present climate forecasts was uncovered. Understanding the limitations to information use forms a major component of this study. The complexity of the management environment, the lack of well-defined linkages among potential users and forecasters, and the lack of supplementary background information relating to the forecasts pose substantial barriers to future use of forecasts. Recommendations to address these problems are offered. The use of climate information and forecasts to reduce the uncertainty inherent in managing large systems for diverse needs bears significant promise.

Medium- and long-term electric power demand forecasting based on the big data of smart city

NASA Astrophysics Data System (ADS)

Wei, Zhanmeng; Li, Xiyuan; Li, Xizhong; Hu, Qinghe; Zhang, Haiyang; Cui, Pengjie

2017-08-01

Based on the smart city, this paper proposed a new electric power demand forecasting model, which integrates external data such as meteorological information, geographic information, population information, enterprise information and economic information into the big database, and uses an improved algorithm to analyse the electric power demand and provide decision support for decision makers. The data mining technology is used to synthesize kinds of information, and the information of electric power customers is analysed optimally. The scientific forecasting is made based on the trend of electricity demand, and a smart city in north-eastern China is taken as a sample.

Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada

EPA Science Inventory

A Beneficial Use Impairment (BUI) common at Great Lakes Areas of Concern (AOCs) is loss of fish and wildlife populations. Consequently, recovery of populations after stressor mitigation serves as a basis for evaluating remediation success. We describe a framework that can be a...

Megaregion freight movements : a case study of the Texas Triangle.

DOT National Transportation Integrated Search

2011-09-01

U.S. population growth is predicted to substantially increase over the next 40 years, particularly in areas with large regional economies forecasted to contain over two-thirds of the national economic activity. In Texas, population growth from 2000 t...

Mega-region freight movements : a case study of the Texas triangle.

DOT National Transportation Integrated Search

2011-09-01

U.S. population growth is predicted to substantially increase over the next 40 years, particularly in areas : with large regional economies forecasted to contain over two-thirds of the national economic activity. In : Texas, population growth from 20...

Cod Collapse and the Climate in the North Atlantic

NASA Astrophysics Data System (ADS)

Meng, K. C.; Oremus, K. L.; Gaines, S.

2014-12-01

Effective fisheries management requires forecasting population changes. We find a negative relationship between the North Atlantic Oscillation (NAO) index and subsequently surveyed biomass and catch of Atlantic cod, Gadus morhua, off the New England coast. A 1-unit NAO increase is associated with a 17% decrease in surveyed biomass of age-1 cod the following year. This relationship persists as the cod mature, such that observed NAO can be used to forecast future adult biomass. We also document that an NAO event lowers catch for up to 15 years afterward. In contrast to forecasts by existing stock assessment models, our NAO-driven statistical model successfully hindcasts the recent collapse of New England cod fisheries following strong NAO events in 2007 and 2008 (see figure). This finding can serve as a template for forecasting other fisheries affected by climatic conditions.

Forecasting Cause-Specific Mortality in Korea up to Year 2032

PubMed Central

2016-01-01

Forecasting cause-specific mortality can help estimate the future burden of diseases and provide a clue for preventing diseases. Our objective was to forecast the mortality for causes of death in the future (2013-2032) based on the past trends (1983-2012) in Korea. The death data consisted of 12 major causes of death from 1983 to 2012 and the population data consisted of the observed and estimated populations (1983-2032) in Korea. The modified age-period-cohort model with an R-based program, nordpred software, was used to forecast future mortality. Although the age-standardized rates for the world standard population for both sexes are expected to decrease from 2008-2012 to 2028-2032 (males: -31.4%, females: -32.3%), the crude rates are expected to increase (males: 46.3%, females: 33.4%). The total number of deaths is also estimated to increase (males: 52.7%, females: 41.9%). Additionally, the largest contribution to the overall change in deaths was the change in the age structures. Several causes of death are projected to increase in both sexes (cancer, suicide, heart diseases, pneumonia and Alzheimer’s disease), while others are projected to decrease (cerebrovascular diseases, liver diseases, diabetes mellitus, traffic accidents, chronic lower respiratory diseases, and pulmonary tuberculosis). Cancer is expected to be the highest cause of death for both the 2008-2012 and 2028-2032 time periods in Korea. To reduce the disease burden, projections of the future cause-specific mortality should be used as fundamental data for developing public health policies. PMID:27478326

Moving beyond the cost-loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker

NASA Astrophysics Data System (ADS)

Matte, Simon; Boucher, Marie-Amélie; Boucher, Vincent; Fortier Filion, Thomas-Charles

2017-06-01

A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost-loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed deterministic forecasts, forecasts based on meteorological ensembles, and a variant of the latter that also includes an estimation of state variable uncertainty. This comparison takes place for the Montmorency River, a small flood-prone watershed in southern central Quebec, Canada. The assessment of forecasts is performed for lead times of 1 to 5 days, both in terms of forecasts' quality (relative to the corresponding record of observations) and in terms of economic value, using the new proposed framework based on the CARA utility function. It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. Hence, post-processing forecasts to avoid over-forecasting could help improve both the quality and the value of forecasts.

Computer simulation of the coffee leaf miner using sexual Penna aging model

NASA Astrophysics Data System (ADS)

de Oliveira, A. C. S.; Martins, S. G. F.; Zacarias, M. S.

2008-01-01

Forecast models based on climatic conditions are of great interest in Integrated Pest Management (IPM) programs. The success of these models depends, among other factors, on the knowledge of the temperature effect on the pests’ population dynamics. In this direction, a computer simulation was made for the population dynamics of the coffee leaf miner, L. coffeella, at different temperatures, considering experimental data relative to the pest. The age structure was inserted into the dynamics through sexual Penna Model. The results obtained, such as life expectancy, growth rate and annual generations’ number, in agreement to those in laboratory and field conditions, show that the simulation can be used as a forecast model for controlling L. coffeella.

Using demography and movement behavior to predict range expansion of the southern sea otter.

USGS Publications Warehouse

Tinker, M.T.; Doak, D.F.; Estes, J.A.

2008-01-01

In addition to forecasting population growth, basic demographic data combined with movement data provide a means for predicting rates of range expansion. Quantitative models of range expansion have rarely been applied to large vertebrates, although such tools could be useful for restoration and management of many threatened but recovering populations. Using the southern sea otter (Enhydra lutris nereis) as a case study, we utilized integro-difference equations in combination with a stage-structured projection matrix that incorporated spatial variation in dispersal and demography to make forecasts of population recovery and range recolonization. In addition to these basic predictions, we emphasize how to make these modeling predictions useful in a management context through the inclusion of parameter uncertainty and sensitivity analysis. Our models resulted in hind-cast (1989–2003) predictions of net population growth and range expansion that closely matched observed patterns. We next made projections of future range expansion and population growth, incorporating uncertainty in all model parameters, and explored the sensitivity of model predictions to variation in spatially explicit survival and dispersal rates. The predicted rate of southward range expansion (median = 5.2 km/yr) was sensitive to both dispersal and survival rates; elasticity analysis indicated that changes in adult survival would have the greatest potential effect on the rate of range expansion, while perturbation analysis showed that variation in subadult dispersal contributed most to variance in model predictions. Variation in survival and dispersal of females at the south end of the range contributed most of the variance in predicted southward range expansion. Our approach provides guidance for the acquisition of further data and a means of forecasting the consequence of specific management actions. Similar methods could aid in the management of other recovering populations.

Long-Term Climate Forcing in Loggerhead Sea Turtle Nesting

PubMed Central

Van Houtan, Kyle S.; Halley, John M.

2011-01-01

The long-term variability of marine turtle populations remains poorly understood, limiting science and management. Here we use basin-scale climate indices and regional surface temperatures to estimate loggerhead sea turtle (Caretta caretta) nesting at a variety of spatial and temporal scales. Borrowing from fisheries research, our models investigate how oceanographic processes influence juvenile recruitment and regulate population dynamics. This novel approach finds local populations in the North Pacific and Northwest Atlantic are regionally synchronized and strongly correlated to ocean conditions—such that climate models alone explain up to 88% of the observed changes over the past several decades. In addition to its performance, climate-based modeling also provides mechanistic forecasts of historical and future population changes. Hindcasts in both regions indicate climatic conditions may have been a factor in recent declines, but future forecasts are mixed. Available climatic data suggests the Pacific population will be significantly reduced by 2040, but indicates the Atlantic population may increase substantially. These results do not exonerate anthropogenic impacts, but highlight the significance of bottom-up oceanographic processes to marine organisms. Future studies should consider environmental baselines in assessments of marine turtle population variability and persistence. PMID:21589639

Long-term climate forcing in loggerhead sea turtle nesting.

PubMed

Van Houtan, Kyle S; Halley, John M

2011-04-27

The long-term variability of marine turtle populations remains poorly understood, limiting science and management. Here we use basin-scale climate indices and regional surface temperatures to estimate loggerhead sea turtle (Caretta caretta) nesting at a variety of spatial and temporal scales. Borrowing from fisheries research, our models investigate how oceanographic processes influence juvenile recruitment and regulate population dynamics. This novel approach finds local populations in the North Pacific and Northwest Atlantic are regionally synchronized and strongly correlated to ocean conditions--such that climate models alone explain up to 88% of the observed changes over the past several decades. In addition to its performance, climate-based modeling also provides mechanistic forecasts of historical and future population changes. Hindcasts in both regions indicate climatic conditions may have been a factor in recent declines, but future forecasts are mixed. Available climatic data suggests the Pacific population will be significantly reduced by 2040, but indicates the Atlantic population may increase substantially. These results do not exonerate anthropogenic impacts, but highlight the significance of bottom-up oceanographic processes to marine organisms. Future studies should consider environmental baselines in assessments of marine turtle population variability and persistence.

Population projections for three counties in Zhejiang Province.

PubMed

Zhuang, B; Huang, X

1983-01-01

Using population numbers and deaths in each age group in Yayao, Jiangshan, and Huangyan counties, China, in 1978, the authors analyze current population dynamics and project population trends for the next 20-60 years. The total population of the 3 counties is 2,314,566, with 33.2% 0-14 years old and 5.7% over 65 years old. The dependency ratio is 63.7%. 24.16% of the women are of childbearing age, 15-49 years old. The birth rate averages 15.39% and the mortality rate is 5.91%. Life expectancy is 68.94 for males and 71.94 for females. Males account for 51.6% of the population and females 48.4%, primarily due to the preferential treatment given to male babies. 3 constrictions in the age pyramid reflect conditions caused by the Japanese invasion of 1941-1945, economic policy blunders during the Great Leap Forward and natural disasters, and, most recently, the family planning program. The recent 1 child family policy aims to limit China's total population to 1.2 billion by the year 2000. Achieving this goal requires careful population planning based on actual local conditions. 3 forecasts--based on different combinations of 1 and 2 child families--estimate total birth rates of 1.46, 1.184, and 1.925. These assumptions produce natural increase rates of 5.66%, 7.83%, and 10.64%. All 3 forecasts produce an aging population, but the dependency ratio decreases. China's population policy must be based on the fact that the current population is 1 billion, 800 million of whom are peasants, and that China has too little arable land and is economically undeveloped. The authors consider forecast 1--in which couples have 2 children each from 1981-1985 and half have 1 and half have 2 child families from 1986-2000--the most desirable because 1) it will be acceptable to the peasant population, 2) it maintains a large labor force, 3) it produces a stable age pyramid, and 4) it remains a reasonable possibility.

Forecasting effects of climate change on Great Lakes fisheries: models that link habitat supply to population dynamics can help

USGS Publications Warehouse

Jones, Michael L.; Shuter, Brian J.; Zhao, Yingming; Stockwell, Jason D.

2006-01-01

Future changes to climate in the Great Lakes may have important consequences for fisheries. Evidence suggests that Great Lakes air and water temperatures have risen and the duration of ice cover has lessened during the past century. Global circulation models (GCMs) suggest future warming and increases in precipitation in the region. We present new evidence that water temperatures have risen in Lake Erie, particularly during summer and winter in the period 1965–2000. GCM forecasts coupled with physical models suggest lower annual runoff, less ice cover, and lower lake levels in the future, but the certainty of these forecasts is low. Assessment of the likely effects of climate change on fish stocks will require an integrative approach that considers several components of habitat rather than water temperature alone. We recommend using mechanistic models that couple habitat conditions to population demographics to explore integrated effects of climate-caused habitat change and illustrate this approach with a model for Lake Erie walleye (Sander vitreum). We show that the combined effect on walleye populations of plausible changes in temperature, river hydrology, lake levels, and light penetration can be quite different from that which would be expected based on consideration of only a single factor.

Operational value of ensemble streamflow forecasts for hydropower production: A Canadian case study

NASA Astrophysics Data System (ADS)

Boucher, Marie-Amélie; Tremblay, Denis; Luc, Perreault; François, Anctil

2010-05-01

Ensemble and probabilistic forecasts have many advantages over deterministic ones, both in meteorology and hydrology (e.g. Krzysztofowicz, 2001). Mainly, they inform the user on the uncertainty linked to the forecast. It has been brought to attention that such additional information could lead to improved decision making (e.g. Wilks and Hamill, 1995; Mylne, 2002; Roulin, 2007), but very few studies concentrate on operational situations involving the use of such forecasts. In addition, many authors have demonstrated that ensemble forecasts outperform deterministic forecasts in terms of performance (e.g. Jaun et al., 2005; Velazquez et al., 2009; Laio and Tamea, 2007). However, such performance is mostly assessed on the basis of numerical scoring rules, which compare the forecasts to the observations, and seldom in terms of management gains. The proposed case study adopts an operational point of view, on the basis that a novel forecasting system has value only if it leads to increase monetary and societal gains (e.g. Murphy, 1994; Laio and Tamea, 2007). More specifically, Environment Canada operational ensemble precipitation forecasts are used to drive the HYDROTEL distributed hydrological model (Fortin et al., 1995), calibrated on the Gatineau watershed located in Québec, Canada. The resulting hydrological ensemble forecasts are then incorporated into Hydro-Québec SOHO stochastic management optimization tool that automatically search for optimal operation decisions for the all reservoirs and hydropower plants located on the basin. The timeline of the study is the fall season of year 2003. This period is especially relevant because of high precipitations that nearly caused a major spill, and forced the preventive evacuation of a portion of the population located near one of the dams. We show that the use of the ensemble forecasts would have reduced the occurrence of spills and flooding, which is of particular importance for dams located in populous area, and increased hydropower production. The ensemble precipitation forecasts extend from March 1st of 2002 to December 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used within HYDROTEL in order to compare ensemble streamflow forecasts with their deterministic counterparts. Although this study does not incorporate all the sources of uncertainty, precipitation is certainly the most important input for hydrological modeling and conveys a great portion of the total uncertainty. References: Fortin, J.P., Moussa, R., Bocquillon, C. and Villeneuve, J.P. 1995: HYDROTEL, un modèle hydrologique distribué pouvant bénéficier des données fournies par la télédétection et les systèmes d'information géographique, Revue des Sciences de l'Eau, 8(1), 94-124. Jaun, S., Ahrens, B., Walser, A., Ewen, T. and Schaer, C. 2008: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Natural Hazards and Earth System Sciences, 8 (2), 281-291. Krzysztofowicz, R. 2001: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9. Murphy, A.H. 1994: Assessing the economic value of weather forecasts: An overview of methods, results and issues, Meteorological Applications, 1, 69-73. Mylne, K.R. 2002: Decision-Making from probability forecasts based on forecast value, Meteorological Applications, 9, 307-315. Laio, F. and Tamea, S. 2007: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrology and Earth System Sciences, 11, 1267-1277. Roulin, E. 2007: Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrology and Earth System Sciences, 11, 725-737. Velazquez, J.-A., Petit, T., Lavoie, A., Boucher, M.-A., Turcotte, R., Fortin, V. and Anctil, F. 2009: An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting, Hydrology and Earth System Sciences, 13(11), 2221-2231. Wilks, D.S. and Hamill, T.M. 1995: Potential economic value of ensemble-based surface weather forecasts, Monthly Weather Review, 123(12), 3565-3575.

Forecasting carbon dioxide emissions.

PubMed

Zhao, Xiaobing; Du, Ding

2015-09-01

This study extends the literature on forecasting carbon dioxide (CO2) emissions by applying the reduced-form econometrics approach of Schmalensee et al. (1998) to a more recent sample period, the post-1997 period. Using the post-1997 period is motivated by the observation that the strengthening pace of global climate policy may have been accelerated since 1997. Based on our parameter estimates, we project 25% reduction in CO2 emissions by 2050 according to an economic and population growth scenario that is more consistent with recent global trends. Our forecasts are conservative due to that we do not have sufficient data to fully take into account recent developments in the global economy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

NASA Astrophysics Data System (ADS)

Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

2013-12-01

The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in near-time hydrologic forecast applications (Hirpa et al. 2013). More recent efforts during this year's monsoon season are optimally combining these different independent sources of river forecast information along with archived flood inundation imagery of the Dartmouth Flood Observatory to improve the visualization and overall skill of the ongoing CFAB ensemble weather forecast-based flood forecasting system within the unique context of the ongoing flood forecasting efforts for Bangladesh.

Prediction of the demographic situation in urban districts as a factor of sustainable social and economic development of the transport infrastructure

NASA Astrophysics Data System (ADS)

Davydova, Tatyana; Zhutaeva, Evgeniya; Dubrovskaya, Tatyana

2017-10-01

Article considers the significance of the demographic forecast for the effective operation of the providing system of social and economic development of the urban transport infrastructure. Analysis of the factors which influence on the population of the city of Voronezh was performed and the population forecast for the year 2020 is presented on the basis of the classification by year of birth. Calculation was performed in three variants (with consideration of the use of classification by year of birth) in connection with an impact of modern social and economic situation on the negative tendencies formed in demographic processes. In the basis of variants were grounded different approaches to the dynamics of demographic processes. The main demographic indicators are the number of permanent residents, birth rates, death rates, migration rates. According to the results of the study, population of the urban district of the city of Voronezh is expected to increase in the specified period and migration inflow of the population has a dominant role in the formation in the formation of the number of the city population.

Probabilistic population aging

PubMed Central

2017-01-01

We merge two methodologies, prospective measures of population aging and probabilistic population forecasts. We compare the speed of change and variability in forecasts of the old age dependency ratio and the prospective old age dependency ratio as well as the same comparison for the median age and the prospective median age. While conventional measures of population aging are computed on the basis of the number of years people have already lived, prospective measures are computed also taking account of the expected number of years they have left to live. Those remaining life expectancies change over time and differ from place to place. We compare the probabilistic distributions of the conventional and prospective measures using examples from China, Germany, Iran, and the United States. The changes over time and the variability of the prospective indicators are smaller than those that are observed in the conventional ones. A wide variety of new results emerge from the combination of methodologies. For example, for Germany, Iran, and the United States the likelihood that the prospective median age of the population in 2098 will be lower than it is today is close to 100 percent. PMID:28636675

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models.

PubMed

Jenouvrier, Stéphanie; Holland, Marika; Stroeve, Julienne; Barbraud, Christophe; Weimerskirch, Henri; Serreze, Mark; Caswell, Hal

2012-09-01

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage-structured, seasonal, nonlinear, two-sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture-mark-recapture analysis, we find that seasonal sea ice concentration anomalies (SICa ) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SICa , because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SICa . We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SICa , which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems. © 2012 Blackwell Publishing Ltd.

Exploration of Urban Spatial Planning Evaluation Based on Humanland Harmony

NASA Astrophysics Data System (ADS)

Hu, X. S.; Ma, Q. R.; Liang, W. Q.; Wang, C. X.; Xiong, X. Q.; Han, X. H.

2017-09-01

This study puts forward a new concept, "population urbanization level forecast - driving factor analysis - urban spatial planning analysis" for achieving efficient and intensive development of urbanization considering human-land harmony. We analyzed big data for national economic and social development, studied the development trends of population urbanization and its influencing factors using the grey system model in Chengmai county of Hainan province, China. In turn, we calculated the population of Chengmai coming years based on the forecasting urbanization rate and the corresponding amount of urban construction land, and evaluated the urban spatial planning with GIS spatial analysis method in the study area. The result shows that the proposed concept is feasible for evaluation of urban spatial planning, and is meaningful for guiding the rational distribution of urban space, controlling the scale of development, improving the quality of urbanization and thus promoting highly-efficient and intensive use of limited land resource.

Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

DOE PAGES

Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; ...

2015-09-30

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID tomore » build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. As a result, our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.« less

Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID tomore » build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. As a result, our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.« less

Agent-Based Model Forecasts Aging of the Population of People Who Inject Drugs in Metropolitan Chicago and Changing Prevalence of Hepatitis C Infections

PubMed Central

Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E.

2015-01-01

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our studies highlight the importance of analyzing subpopulations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities. PMID:26421722

An American Laboratory: Population Growth and Environmental Quality in California.

ERIC Educational Resources Information Center

McConnell, Robert

1993-01-01

Describes the cumulative impact of rapid population growth, industrial and military activity, agriculture, and motor vehicles on California's environmental and social fabric. Discusses these problems in California as a forecast for the nation and test to consensus-based U.S. representative government. (Author/ MCO)

Forecasting disease risk for increased epidemic preparedness in public health

NASA Technical Reports Server (NTRS)

Myers, M. F.; Rogers, D. J.; Cox, J.; Flahault, A.; Hay, S. I.

2000-01-01

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Forecasting Disease Risk for Increased Epidemic Preparedness in Public Health

PubMed Central

Myers, M.F.; Rogers, D.J.; Cox, J.; Flahault, A.; Hay, S.I.

2011-01-01

Emerging infectious diseases pose a growing threat to human populations. Many of the world’s epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector–environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development. PMID:10997211

Forecasting fluid milk and cheese demands for the next decade.

PubMed

Schmit, T M; Kaiser, H M

2006-12-01

Predictions of future market demands and farm prices for dairy products are important determinants in developing marketing strategies and farm-production planning decisions. The objective of this report was to use current aggregate forecast data, combined with existing econometric models of demand and supply, to forecast retail demands for fluid milk and cheese and the supply and price of farm milk over the next decade. In doing so, we can investigate whether projections of population and consumer food-spending patterns will extend or alter current consumption trends and examine the implications of future generic advertising strategies for dairy products. To conduct the forecast simulations and appropriately allocate the farm milk supply to various uses, we used a partial equilibrium model of the US domestic dairy sector that segmented the industry into retail, wholesale, and farm markets. Model simulation results indicated that declines in retail per capita demand would persist but at a reduced rate from years past and that retail per capita demand for cheese would continue to grow and strengthen over the next decade. These predictions rely on expected changes in the size of populations of various ages, races, and ethnicities and on existing patterns of spending on food at home and away from home. The combined effect of these forecasted changes in demand levels was reflected in annualized growth in the total farm-milk supply that was similar to growth realized during the past few years. Although we expect nominal farm milk prices to increase over the next decade, we expect real prices (relative to assumed growth in feed costs) to remain relatively stable and show no increase until the end of the forecast period. Supplemental industry model simulations also suggested that net losses in producer revenues would result if only nominal levels of generic advertising spending were maintained in forthcoming years. In fact, if real generic advertising expenditures are increased relative to 2005 levels, returns to the investment in generic advertising can be improved. Specifically, each additional real dollar invested in generic advertising for fluid milk and cheese products over the forecast period would result in an additional 5.61 dollars in producer revenues.

COP21 climate negotiators' responses to climate model forecasts

NASA Astrophysics Data System (ADS)

Bosetti, Valentina; Weber, Elke; Berger, Loïc; Budescu, David V.; Liu, Ning; Tavoni, Massimo

2017-02-01

Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most effectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models' forecasts. We randomized the way information was provided across participants using three different formats similar to those used in Intergovernmental Panel on Climate Change reports. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more effective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models' forecasts than policymakers. There was also no effect of presentation format in the MBA sample. These results highlight the importance of testing visualization tools directly on the population of interest.

Air Traffic Forecasting at the Port Authority of New York and New Jersey

NASA Technical Reports Server (NTRS)

Augustine, J. G.

1972-01-01

Procedures for conducting air traffic forecasts with specific application to the Port Authority of New York and New Jersey are discussed. The procedure relates air travel growth to detailed socio-economic and demographic characteristics of the U.S. population rather than to aggregate economic data such as Gross National Product, personal income, and industrial production. Charts are presented to show the relationship between various selected characteristics and the use of air transportation facilities.

Future Trends in San Diego: Population, Income, Employment, Post College Wages and Enrollment.

ERIC Educational Resources Information Center

Barnes, Randy; Armstrong, William B.; Bersentes, Gina; Turingan, Maria

This report contains forecasted data for San Diego through the year 2015 and examines changes that have taken place over the past fifty years. Historically, San Diego population growth rates have been relatively high compared with the rest of the nation. Between 1998 and 2015, the population will not only become larger, it will become more…

Contrasting patterns of nest survival and postfledging survival in ovenbirds and Acadian flycatchers in Missouri forest fragments

Treesearch

Julianna M. A. Jenkins; Frank R. Thompson; John Faaborg

2016-01-01

We can improve our ability to assess population viability and forecast population growth under different scenarios by understanding factors that limit population parameters in each stage of the annual cycle. Postfledging mortality rates may be as variable as nest survival across regions and fragmentation gradients, although factors that negatively impact nest survival...

Application of seasonal forecasting for the drought forecasting in Catalonia (Spain)

NASA Astrophysics Data System (ADS)

Llasat, Maria-Carmen; Zaragoza, Albert; Aznar, Blanca; Cabot, Jordi

2010-05-01

Low flows and droughts are a hydro-climatic feature in Spain (Alvarez et al, 2008). The construction of dams as water reservoirs has been a usual tool to manage the water resources for agriculture and livestock, industries and human needs (MIMAM, 2000, 2007). The last drought that has affected Spain has last four years in Catalonia, from 2004 to the spring of 2008, and it has been particularly hard as a consequence of the precipitation deficit in the upper part of the rivers that nourish the main dams. This problem increases when the water scarcity affects very populated areas, like big cities. The Barcelona city, with more than 3.000.000 people concentrated in the downtown and surrounding areas is a clear example. One of the objectives of the SOSTAQUA project is to improve the water resources management in real time, in order to improve the water supply in the cities in the framework of sustainable development. The work presented here deals with the application of seasonal forecasting to improve the water management in Catalonia, particularly in drought conditions. A seasonal prediction index has been created as a linear combination of climatic data and the ECM4 prediction that has been validated too. This information has implemented into a hydrological model and it has been applied to the last drought considering the real water demands of population, as well as to the water storage evolution in the last months. It has been found a considerable advance in the forecasting of water volume into reservoirs. The advantage of this methodology is that it only requires seasonal forecasting free through internet. Due to the fact that the principal rivers that supply water to Barcelona, birth on the Pyrenees and Pre-Pyrenees region, the analysis and precipitation forecasting is focused on this region (Zaragoza, 2008).

Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.

PubMed

Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo

2016-01-01

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.

Evaluation of a wildfire smoke forecasting system as a tool for public health protection.

PubMed

Yao, Jiayun; Brauer, Michael; Henderson, Sarah B

2013-10-01

Exposure to wildfire smoke has been associated with cardiopulmonary health impacts. Climate change will increase the severity and frequency of smoke events, suggesting a need for enhanced public health protection. Forecasts of smoke exposure can facilitate public health responses. We evaluated the utility of a wildfire smoke forecasting system (BlueSky) for public health protection by comparing its forecasts with observations and assessing their associations with population-level indicators of respiratory health in British Columbia, Canada. We compared BlueSky PM2.5 forecasts with PM2.5 measurements from air quality monitors, and BlueSky smoke plume forecasts with plume tracings from National Oceanic and Atmospheric Administration Hazard Mapping System remote sensing data. Daily counts of the asthma drug salbutamol sulfate dispensations and asthma-related physician visits were aggregated for each geographic local health area (LHA). Daily continuous measures of PM2.5 and binary measures of smoke plume presence, either forecasted or observed, were assigned to each LHA. Poisson regression was used to estimate the association between exposure measures and health indicators. We found modest agreement between forecasts and observations, which was improved during intense fire periods. A 30-μg/m3 increase in BlueSky PM2.5 was associated with an 8% increase in salbutamol dispensations and a 5% increase in asthma-related physician visits. BlueSky plume coverage was associated with 5% and 6% increases in the two health indicators, respectively. The effects were similar for observed smoke, and generally stronger in very smoky areas. BlueSky forecasts showed modest agreement with retrospective measures of smoke and were predictive of respiratory health indicators, suggesting they can provide useful information for public health protection.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): assessing the added value of probabilistic forecasts

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2012-04-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on deterministic (COSMO-7) and probabilistic (COSMO-LEPS) atmospheric forecasts, which are used to force a semi-distributed hydrological model (PREVAH) coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which we assessed the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added value conveyed by the probability information, a 31-month reforecast was produced for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain is of up to 2 days lead time for the catchment considered. Brier skill scores show that probabilistic hydrological forecasts outperform their deterministic counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. We finally highlight challenges for making decisions on the basis of hydrological predictions, and discuss the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Geographic range and structure of cryptic genetic diversity among Pacific North American populations of the non-native amphipod Grandidierella japonica

EPA Science Inventory

Reconstructing the invasion history of aquatic invasive species can enhance understanding of invasion risks by recognizing areas most susceptible to invasion and forecasting future spread based on past patterns of population expansion. Here we reconstruct the invasion history of ...

Daily Reservoir Inflow Forecasting using Deep Learning with Downscaled Multi-General Circulation Models (GCMs) Platform

NASA Astrophysics Data System (ADS)

Li, D.; Fang, N. Z.

2017-12-01

Dallas-Fort Worth Metroplex (DFW) has a population of over 7 million depending on many water supply reservoirs. The reservoir inflow plays a vital role in water supply decision making process and long-term strategic planning for the region. This paper demonstrates a method of utilizing deep learning algorithms and multi-general circulation model (GCM) platform to forecast reservoir inflow for three reservoirs within the DFW: Eagle Mountain Lake, Lake Benbrook and Lake Arlington. Ensemble empirical mode decomposition was firstly employed to extract the features, which were then represented by the deep belief networks (DBNs). The first 75 years of the historical data (1940 -2015) were used to train the model, while the last 2 years of the data (2016-2017) were used for the model validation. The weights of each DBN gained from the training process were then applied to establish a neural network (NN) that was able to forecast reservoir inflow. Feature predictors used for the forecasting model were generated from weather forecast results of the downscaled multi-GCM platform for the North Texas region. By comparing root mean square error (RMSE) and mean bias error (MBE) with the observed data, the authors found that the deep learning with downscaled multi-GCM platform is an effective approach in the reservoir inflow forecasting.

Integrated Drought Monitoring and Forecasts for Decision Making in Water and Agricultural Sectors over the Southeastern US under Changing Climate

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Ward, R.

2017-12-01

Changing climate arising from structured oscillations such as ENSO and rising temperature poses challenging issues in meeting the increasing water demand (due to population growth) for public supply and agriculture over the Southeast US. This together with infrastructural (e.g., most reservoirs being within-year systems) and operational (e.g., static rule curves) constraints requires an integrated approach that seamlessly monitors and forecasts water and soil moisture conditions to support adaptive decision making in water and agricultural sectors. In this talk, we discuss the utility of an integrated drought management portal that both monitors and forecasts streamflow and soil moisture over the southeast US. The forecasts are continuously developed and updated by forcing monthly-to-seasonal climate forecasts with a land surface model for various target basins. The portal also houses a reservoir allocation model that allows water managers to explore different release policies in meeting the system constraints and target storages conditioned on the forecasts. The talk will also demonstrate how past events (e.g., 2007-2008 drought) could be proactively monitored and managed to improve decision making in water and agricultural sectors over the Southeast US. Challenges in utilizing the portal information from institutional and operational perspectives will also be presented.

Communicating Storm Surge Forecast Uncertainty

NASA Astrophysics Data System (ADS)

Troutman, J. A.; Rhome, J.

2015-12-01

When it comes to tropical cyclones, storm surge is often the greatest threat to life and property along the coastal United States. The coastal population density has dramatically increased over the past 20 years, putting more people at risk. Informing emergency managers, decision-makers and the public about the potential for wind driven storm surge, however, has been extremely difficult. Recently, the Storm Surge Unit at the National Hurricane Center in Miami, Florida has developed a prototype experimental storm surge watch/warning graphic to help communicate this threat more effectively by identifying areas most at risk for life-threatening storm surge. This prototype is the initial step in the transition toward a NWS storm surge watch/warning system and highlights the inundation levels that have a 10% chance of being exceeded. The guidance for this product is the Probabilistic Hurricane Storm Surge (P-Surge) model, which predicts the probability of various storm surge heights by statistically evaluating numerous SLOSH model simulations. Questions remain, however, if exceedance values in addition to the 10% may be of equal importance to forecasters. P-Surge data from 2014 Hurricane Arthur is used to ascertain the practicality of incorporating other exceedance data into storm surge forecasts. Extracting forecast uncertainty information through analyzing P-surge exceedances overlaid with track and wind intensity forecasts proves to be beneficial for forecasters and decision support.

Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Intharathirat, Rotchana, E-mail: rotchana.in@gmail.com; Abdul Salam, P., E-mail: salam@ait.ac.th; Kumar, S., E-mail: kumar@ait.ac.th

Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developingmore » countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.« less

Over two decades of Plasmodium knowlesi infections in Sarawak: Trend and forecast.

PubMed

Ooi, Choo Huck; Bujang, Mohamad Adam; Tg Abu Bakar Sidik, Tg Mohd Ikhwan; Ngui, Romano; Lim, Yvonne Ai-Lian

2017-12-01

Malaria is still of great public health concern, especially in Malaysian Borneo. The aim of this study was to determine the trends of P. knowlesi infection in Sarawak, Malaysia and to forecast the incidence of P. knowlesi until the year 2040. Data on P. knowlesi malaria cases from 1992 to the year 2014 were obtained from the Sarawak Health Department, Malaysia. ARIMA model was applied to forecast the future incidence of P. knowlesi infection. The data for the whole of Sarawak and subsequently the selected six districts which have high incidence rates of P. knowlesi infection were analyzed. Results of the analysis showed that there was an increasing trend of P. knowlesi cases from the year 1992-2014 (p<0.001). The trend in the incidence started to increase in the year 2008 (p=0.029). The incidence rate per 100,000 populations was between 4.15 in the year 1992 and 42.03 in the year 2014. High incidence of P. knowlesi infections has been detected in the districts adjacent to each other within the interior region of Sarawak. The forecasted incidence and incidence rate per 100,000 populations in the year 2020 were 1229 and 44.04, respectively, while those in the year 2040 were 2056 and 62.91, respectively. The forecasted incidence showed an upward trend highlighting an urgent need to draw up strategic and holistic prevention plans to limit further the increase in P. knowlesi morbidity and mortality in Sarawak. It is imperative that these measures are customized taking into consideration the challenges faced in the interior areas of Sarawak and the behavior of the main vector of P. knowlesi (i.e., An. latens) in Sarawak. Copyright © 2017 Elsevier B.V. All rights reserved.

Incidence Rate of Acute Encephalitis Syndrome without Specific Treatment in India and Nepal

PubMed Central

Potharaju, Nagabhushana Rao

2012-01-01

Background: A performance target (PT) for the incidence rate (IR) of acute encephalitis syndrome (AES) was not defined by the World Health Organization (WHO) due to lack of data. There is no specific treatment for ~90% of the AES cases. Objectives: (1) To determine the IR of AES not having specific treatment (AESn) in two countries, India and Nepal. (2) To suggest the PT. Subjects and Methods: This was a record-based study of the entire population of India and Nepal from 1978 to 2011. The WHO definition was used for inclusion of cases. Cases that had specific treatment were excluded. IR was calculated per 100,000 population per annum. Forecast IR was generated from 2010 to 2013 using time-series analysis. Results: There were 165,461 cases from 1978 to 2011, of which 125,030 cases were from India and 40,431 were from Nepal. The mean IR of India was 0.42 (s 0.24) and that of Nepal was 5.23 (σ 3.03). IRs of 2010 and 2011 of India and that of 2011 of Nepal were closer to the mean IR rather than the forecast IR. IR of 2010 of Nepal was closer to the forecast IR. The forecast IR for India for 2012 was 0.49 (0.19-1.06), for 2013 was 0.42 (0.15-0.97) and for Nepal for both 2012 and 2013 was 5.62 (1.53-15.05). Conclusions: IRs were considerably different for India and Nepal. Using the current mean IR as PT for the next year was simple and practical. Using forecasting was complex and, less frequently, useful. PMID:23293439

Forecasts of health care utilization related to pandemic A(H1N1)2009 influenza in the Nord-Pas-de-Calais region, France.

PubMed

Giovannelli, J; Loury, P; Lainé, M; Spaccaferri, G; Hubert, B; Chaud, P

2015-05-01

To describe and evaluate the forecasts of the load that pandemic A(H1N1)2009 influenza would have on the general practitioners (GP) and hospital care systems, especially during its peak, in the Nord-Pas-de-Calais (NPDC) region, France. Modelling study. The epidemic curve was modelled using an assumption of normal distribution of cases. The values for the forecast parameters were estimated from a literature review of observed data from the Southern hemisphere and French Overseas Territories, where the pandemic had already occurred. Two scenarios were considered, one realistic, the other pessimistic, enabling the authors to evaluate the 'reasonable worst case'. Forecasts were then assessed by comparing them with observed data in the NPDC region--of 4 million people. The realistic scenarios forecasts estimated 300,000 cases, 1500 hospitalizations, 225 intensive care units (ICU) admissions for the pandemic wave; 115 hospital beds and 45 ICU beds would be required per day during the peak. The pessimistic scenario's forecasts were 2-3 times higher than the realistic scenario's forecasts. Observed data were: 235,000 cases, 1585 hospitalizations, 58 ICU admissions; and a maximum of 11.6 ICU beds per day. The realistic scenario correctly estimated the temporal distribution of GP and hospitalized cases but overestimated the number of cases admitted to ICU. Obtaining more robust data for parameters estimation--particularly the rate of ICU admission among the population that the authors recommend to use--may provide better forecasts. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Forecasting the 2013–2014 influenza season using Wikipedia

DOE PAGES

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; ...

2015-05-14

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis

NASA Astrophysics Data System (ADS)

de Weger, Letty A.; Beerthuizen, Thijs; Hiemstra, Pieter S.; Sont, Jacob K.

2014-08-01

One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature ( R 2 = 0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures ( R 2 = 0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.

Forecasting the 2013–2014 Influenza Season Using Wikipedia

PubMed Central

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.

2015-01-01

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed. PMID:25974758

Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis.

PubMed

de Weger, Letty A; Beerthuizen, Thijs; Hiemstra, Pieter S; Sont, Jacob K

2014-08-01

One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature (R (2)=0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures (R (2)=0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.

Forecasting the 2013-2014 influenza season using Wikipedia.

PubMed

Hickmann, Kyle S; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M; Deshpande, Alina; Del Valle, Sara Y

2015-05-01

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

Forecasting the 2013–2014 influenza season using Wikipedia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

How Many Kentuckians: Population Forecasts, 1980-2020. The 1986 Edition.

ERIC Educational Resources Information Center

Price, Michael

A Kentucky population projection presents 1980 census counts and projections for 1985, 1990, 1995, 2000, 2010, and 2020 for the state, its 15 area development districts, and its 120 counties. Populations are broken down by gender and 5-year age groups through 85 years and over, with age summaries for 0-18 years, 19-64 years, and 65 years and over.…

Forecasting the need for physicians in the United States: the Health Resources and Services Administration's physician requirements model.

PubMed Central

Greenberg, L; Cultice, J M

1997-01-01

OBJECTIVE: The Health Resources and Services Administration's Bureau of Health Professions developed a demographic utilization-based model of physician specialty requirements to explore the consequences of a broad range of scenarios pertaining to the nation's health care delivery system on need for physicians. DATA SOURCE/STUDY SETTING: The model uses selected data primarily from the National Center for Health Statistics, the American Medical Association, and the U.S. Bureau of Census. Forecasts are national estimates. STUDY DESIGN: Current (1989) utilization rates for ambulatory and inpatient medical specialty services were obtained for the population according to age, gender, race/ethnicity, and insurance status. These rates are used to estimate specialty-specific total service utilization expressed in patient care minutes for future populations and converted to physician requirements by applying per-physician productivity estimates. DATA COLLECTION/EXTRACTION METHODS: Secondary data were analyzed and put into matrixes for use in the mainframe computer-based model. Several missing data points, e.g., for HMO-enrolled populations, were extrapolated from available data by the project's contractor. PRINCIPAL FINDINGS: The authors contend that the Bureau's demographic utilization model represents improvements over other data-driven methodologies that rely on staffing ratios and similar supply-determined bases for estimating requirements. The model's distinct utility rests in offering national-level physician specialty requirements forecasts. Images Figure 1 PMID:9018213

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2011 CFR

2011-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2012 CFR

2012-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2014 CFR

2014-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2010 CFR

2010-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

Research on water shortage risks and countermeasures in North China

NASA Astrophysics Data System (ADS)

Cheng, Yuxiang; Fang, Wenxuan; Wu, Ziqin

2017-05-01

In the paper, a grey forecasting model and a population growth model are established for forecasting water resources supply and demand situation in the region, and evaluating the scarcity of water resources thereof in order to solve the problem of water shortage in North China. A concrete plan for alleviating water resources pressure is proposed with AHP as basis, thereby discussing the feasibility of the plan. Firstly, water resources supply and demand in the future 15 years are predicted. There are four sources for the demand of water resources mainly: industry, agriculture, ecology and resident living. Main supply sources include surface water and underground water resources. A grey forecasting method is adopted for predicting in the paper aiming at water resources demands since industrial, agricultural and ecological water consumption data have excessive decision factors and the correlation is relatively fuzzy. Since residents' water consumption is determined by per capita water consumption and local population, a logistic growth model is adopted to forecast the population. The grey forecasting method is used for predicting per capita water consumption, and total water demand can be obtained finally. International calculation standards are adopted as reference aiming at water supply. The grey forecasting method is adopted for forecasting surface water quantity and underground water quantity, and water resources supply is obtained finally. Per capita water availability in the region is calculated by comparing the water resources supply and demand. Results show that per capita water availability in the region is only 283 cubic meters this year, people live in serious water shortage region, who will suffer from water shortage state for long time. Then, sensitivity analysis is applied for model test. The test result is excellent, and the prediction results are more accurate. In the paper, the following measures are proposed for improving water resources condition in the region according to prediction results, such as construction of reservoirs, sewage treatment, water diversion project and other measures. A detailed water supply plan is formulated. Water supply weights of all measures are determined according to the AHP model. Solution is sought after original models are improved. Results show that water resources quantity per capita will be up to 2170 cubic meters or so this year, people suffer from moderate water shortage in the region, which can meet people's life needs and economic development needs basically. In addition, water resources quantity per capita is increased year by year, and it can reach mild water shortage level after 2030. In a word, local water resources dilemma can be effectively solved by the plan actually, and thoughts can be provided for decision makers.

Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa: lessons and the way forward.

PubMed

Chowell, Gerardo; Viboud, Cécile; Simonsen, Lone; Merler, Stefano; Vespignani, Alessandro

2017-03-01

The unprecedented impact and modeling efforts associated with the 2014-2015 Ebola epidemic in West Africa provides a unique opportunity to document the performances and caveats of forecasting approaches used in near-real time for generating evidence and to guide policy. A number of international academic groups have developed and parameterized mathematical models of disease spread to forecast the trajectory of the outbreak. These modeling efforts often relied on limited epidemiological data to derive key transmission and severity parameters, which are needed to calibrate mechanistic models. Here, we provide a perspective on some of the challenges and lessons drawn from these efforts, focusing on (1) data availability and accuracy of early forecasts; (2) the ability of different models to capture the profile of early growth dynamics in local outbreaks and the importance of reactive behavior changes and case clustering; (3) challenges in forecasting the long-term epidemic impact very early in the outbreak; and (4) ways to move forward. We conclude that rapid availability of aggregated population-level data and detailed information on a subset of transmission chains is crucial to characterize transmission patterns, while ensemble-forecasting approaches could limit the uncertainty of any individual model. We believe that coordinated forecasting efforts, combined with rapid dissemination of disease predictions and underlying epidemiological data in shared online platforms, will be critical in optimizing the response to current and future infectious disease emergencies.

Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system

NASA Astrophysics Data System (ADS)

Liang, Ping; Lin, Hai

2018-02-01

A useful sub-seasonal forecast is of great societal and economical value in the highly populated East Asian region, especially during boreal summer when frequent extreme events such as heat waves and persistent heavy rainfalls occur. Despite recent interest and development in sub-seasonal prediction, it is still unclear how skillful dynamical forecasting systems are in East Asia beyond 2 weeks. In this study we evaluate the sub-seasonal prediction over East Asia during boreal summer in the operational monthly forecasting system of Environment and Climate Change Canada (ECCC).Results show that the climatological intra-seasonal oscillation (CISO) of East Asian summer monsoonis reasonably well captured. Statistically significant forecast skill of 2-meter air temperature (T2m) is achieved for all lead times up to week 4 (days 26-32) over East China and Northeast Asia, which is consistent with the skill in 500 hPa geopotential height (Z500). Significant forecast skill of precipitation, however, is limited to the week of days 5-11. Possible sources of predictability on the sub-seasonal time scale are analyzed. The weekly mean T2m anomaly over East China is found to be linked to an eastward propagating extratropical Rossby wave from the North Atlantic across Europe to East Asia. The Madden-Julian Oscillation (MJO) and El Nino-Southern Oscillation (ENSO) are also likely to influence the forecast skill of T2m at the sub-seasonal timescale over East Asia.

Aging in America in the Twenty-first Century: Demographic Forecasts from the MacArthur Foundation Research Network on an Aging Society

PubMed Central

Olshansky, S Jay; Goldman, Dana P; Zheng, Yuhui; Rowe, John W

2009-01-01

Context: The aging of the baby boom generation, the extension of life, and progressive increases in disability-free life expectancy have generated a dramatic demographic transition in the United States. Official government forecasts may, however, have inadvertently underestimated life expectancy, which would have major policy implications, since small differences in forecasts of life expectancy produce very large differences in the number of people surviving to an older age. This article presents a new set of population and life expectancy forecasts for the United States, focusing on transitions that will take place by midcentury. Methods: Forecasts were made with a cohort-components methodology, based on the premise that the risk of death will be influenced in the coming decades by accelerated advances in biomedical technology that either delay the onset and age progression of major fatal diseases or that slow the aging process itself. Findings: Results indicate that the current forecasts of the U.S. Social Security Administration and U.S. Census Bureau may underestimate the rise in life expectancy at birth for men and women combined, by 2050, from 3.1 to 7.9 years. Conclusions: The cumulative outlays for Medicare and Social Security could be higher by $3.2 to $8.3 trillion relative to current government forecasts. This article discusses the implications of these results regarding the benefits and costs of an aging society and the prospect that health disparities could attenuate some of these changes. PMID:20021588

Short-term ensemble radar rainfall forecasts for hydrological applications

NASA Astrophysics Data System (ADS)

Codo de Oliveira, M.; Rico-Ramirez, M. A.

2016-12-01

Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

Evaluation of a Wildfire Smoke Forecasting System as a Tool for Public Health Protection

PubMed Central

Brauer, Michael; Henderson, Sarah B.

2013-01-01

Background: Exposure to wildfire smoke has been associated with cardiopulmonary health impacts. Climate change will increase the severity and frequency of smoke events, suggesting a need for enhanced public health protection. Forecasts of smoke exposure can facilitate public health responses. Objectives: We evaluated the utility of a wildfire smoke forecasting system (BlueSky) for public health protection by comparing its forecasts with observations and assessing their associations with population-level indicators of respiratory health in British Columbia, Canada. Methods: We compared BlueSky PM2.5 forecasts with PM2.5 measurements from air quality monitors, and BlueSky smoke plume forecasts with plume tracings from National Oceanic and Atmospheric Administration Hazard Mapping System remote sensing data. Daily counts of the asthma drug salbutamol sulfate dispensations and asthma-related physician visits were aggregated for each geographic local health area (LHA). Daily continuous measures of PM2.5 and binary measures of smoke plume presence, either forecasted or observed, were assigned to each LHA. Poisson regression was used to estimate the association between exposure measures and health indicators. Results: We found modest agreement between forecasts and observations, which was improved during intense fire periods. A 30-μg/m3 increase in BlueSky PM2.5 was associated with an 8% increase in salbutamol dispensations and a 5% increase in asthma-related physician visits. BlueSky plume coverage was associated with 5% and 6% increases in the two health indicators, respectively. The effects were similar for observed smoke, and generally stronger in very smoky areas. Conclusions: BlueSky forecasts showed modest agreement with retrospective measures of smoke and were predictive of respiratory health indicators, suggesting they can provide useful information for public health protection. Citation: Yao J, Brauer M, Henderson SB. 2013. Evaluation of a wildfire smoke forecasting system as a tool for public health protection. Environ Health Perspect 121:1142–1147; http://dx.doi.org/10.1289/ehp.1306768 PMID:23906969

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore.

PubMed

Shi, Yuan; Liu, Xu; Kok, Suet-Yheng; Rajarethinam, Jayanthi; Liang, Shaohong; Yap, Grace; Chong, Chee-Seng; Lee, Kim-Sung; Tan, Sharon S Y; Chin, Christopher Kuan Yew; Lo, Andrew; Kong, Waiming; Ng, Lee Ching; Cook, Alex R

2016-09-01

With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369-1375; http://dx.doi.org/10.1289/ehp.1509981.

Flood Forecast Accuracy and Decision Support System Approach: the Venice Case

NASA Astrophysics Data System (ADS)

Canestrelli, A.; Di Donato, M.

2016-02-01

In the recent years numerical models for weather predictions have experienced continuous advances in technology. As a result, all the disciplines making use of weather forecasts have made significant steps forward. In the case of the Safeguard of Venice, a large effort has been put in order to improve the forecast of tidal levels. In this context, the Istituzione Centro Previsioni e Segnalazioni Maree (ICPSM) of the Venice Municipality has developed and tested many different forecast models, both of the statistical and deterministic type, and has shown to produce very accurate forecasts. For Venice, the maximum admissible forecast error should be (ideally) of the order of ten centimeters at 24 hours. The entity of the forecast error clearly affects the decisional process, which mainly consists of alerting the population, activating the movable barriers installed at the three tidal inlets and contacting the port authority. This process becomes more challenging whenever the weather predictions, and therefore the water level forecasts, suddenly change. These new forecasts have to be quickly transformed into operational tasks. Therefore, it is of the utter importance to set up scheduled alerts and emergency plans by means of easy-to-follow procedures. On this direction, Technital has set up a Decision Support System based on expert procedures that minimizes the human mistakes and, as a consequence, reduces the risk of flooding of the historical center. Moreover, the Decision Support System can communicate predefined alerts to all the interested subjects. The System uses the water levels forecasts produced by the ICPSM by taking into account the accuracy at different leading times. The Decision Support System has been successfully tested with 8 years of data, 6 of them in real time. Venice experience shows that the Decision Support System is an essential tool which assesses the risks associated with a particular event, provides clear operational procedures and minimizes the impact of natural floods on human lives, private properties and historical monuments.

Evaluation of traps used to monitor southern pine beetle aerial populations and sex ratios

Treesearch

James T. Cronin; Jane L. Hayes; Peter Turchin

2000-01-01

Various kinds of traps have been employed to monitor and forecast population trends of the southern pine beetle (Dendroctonus frontalis Zimmermann; Coleoptera: Scolytidae), but their accuracy in assessing pine-beetle abundance and sex ratio in the field has not been evaluated directly.In trus study, we...

Recruitment dynamics in complex life cycles. [of organisms living in marine rocky zone

NASA Technical Reports Server (NTRS)

Roughgarden, Jonathan; Possingham, Hugh; Gaines, Steven

1988-01-01

Factors affecting marine population fluctuations are discussed with particular attention given to a common barnacle species of the Pacific coast of North America. It is shown how models combining larval circulation with adult interactions can potentially forecast population fluctuations. These findings demonstrate how processes in different ecological habitats are coupled.

Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece.

PubMed

Moustris, Kostas P; Douros, Konstantinos; Nastos, Panagiotis T; Larissi, Ioanna K; Anthracopoulos, Michael B; Paliatsos, Athanasios G; Priftis, Kostas N

2012-01-01

Artificial Neural Network (ANN) models were developed and applied in order to predict the total weekly number of Childhood Asthma Admission (CAA) at the greater Athens area (GAA) in Greece. Hourly meteorological data from the National Observatory of Athens and ambient air pollution data from seven different areas within the GAA for the period 2001-2004 were used. Asthma admissions for the same period were obtained from hospital registries of the three main Children's Hospitals of Athens. Three different ANN models were developed and trained in order to forecast the CAA for the subgroups of 0-4, 5-14-year olds, and for the whole study population. The results of this work have shown that ANNs could give an adequate forecast of the total weekly number of CAA in relation to the bioclimatic and air pollution conditions. The forecasted numbers are in very good agreement with the observed real total weekly numbers of CAA.

Forecast and analysis of the ratio of electric energy to terminal energy consumption for global energy internet

NASA Astrophysics Data System (ADS)

Wang, Wei; Zhong, Ming; Cheng, Ling; Jin, Lu; Shen, Si

2018-02-01

In the background of building global energy internet, it has both theoretical and realistic significance for forecasting and analysing the ratio of electric energy to terminal energy consumption. This paper firstly analysed the influencing factors of the ratio of electric energy to terminal energy and then used combination method to forecast and analyse the global proportion of electric energy. And then, construct the cointegration model for the proportion of electric energy by using influence factor such as electricity price index, GDP, economic structure, energy use efficiency and total population level. At last, this paper got prediction map of the proportion of electric energy by using the combination-forecasting model based on multiple linear regression method, trend analysis method, and variance-covariance method. This map describes the development trend of the proportion of electric energy in 2017-2050 and the proportion of electric energy in 2050 was analysed in detail using scenario analysis.

Predicting climate effects on Pacific sardine

PubMed Central

Deyle, Ethan R.; Fogarty, Michael; Hsieh, Chih-hao; Kaufman, Les; MacCall, Alec D.; Munch, Stephan B.; Perretti, Charles T.; Ye, Hao; Sugihara, George

2013-01-01

For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine. PMID:23536299

Decadal-scale variation in diet forecasts persistently poor breeding under ocean warming in a tropical seabird

PubMed Central

Tompkins, Emily M.; Townsend, Howard M.

2017-01-01

Climate change effects on population dynamics of natural populations are well documented at higher latitudes, where relatively rapid warming illuminates cause-effect relationships, but not in the tropics and especially the marine tropics, where warming has been slow. Here we forecast the indirect effect of ocean warming on a top predator, Nazca boobies in the equatorial Galápagos Islands, where rising water temperature is expected to exceed the upper thermal tolerance of a key prey item in the future, severely reducing its availability within the boobies’ foraging envelope. From 1983 to 1997 boobies ate mostly sardines, a densely aggregated, highly nutritious food. From 1997 until the present, flying fish, a lower quality food, replaced sardines. Breeding success under the poor diet fell dramatically, causing the population growth rate to fall below 1, indicating a shrinking population. Population growth may not recover: rapid future warming is predicted around Galápagos, usually exceeding the upper lethal temperature and maximum spawning temperature of sardines within 100 years, displacing them permanently from the boobies’ island-constrained foraging range. This provides rare evidence of the effect of ocean warming on a tropical marine vertebrate. PMID:28832597

Decadal-scale variation in diet forecasts persistently poor breeding under ocean warming in a tropical seabird.

PubMed

Tompkins, Emily M; Townsend, Howard M; Anderson, David J

2017-01-01

Climate change effects on population dynamics of natural populations are well documented at higher latitudes, where relatively rapid warming illuminates cause-effect relationships, but not in the tropics and especially the marine tropics, where warming has been slow. Here we forecast the indirect effect of ocean warming on a top predator, Nazca boobies in the equatorial Galápagos Islands, where rising water temperature is expected to exceed the upper thermal tolerance of a key prey item in the future, severely reducing its availability within the boobies' foraging envelope. From 1983 to 1997 boobies ate mostly sardines, a densely aggregated, highly nutritious food. From 1997 until the present, flying fish, a lower quality food, replaced sardines. Breeding success under the poor diet fell dramatically, causing the population growth rate to fall below 1, indicating a shrinking population. Population growth may not recover: rapid future warming is predicted around Galápagos, usually exceeding the upper lethal temperature and maximum spawning temperature of sardines within 100 years, displacing them permanently from the boobies' island-constrained foraging range. This provides rare evidence of the effect of ocean warming on a tropical marine vertebrate.

Forecasting climate change impacts on plant populations over large spatial extents

DOE PAGES

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; ...

2016-10-24

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

USGS Publications Warehouse

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; Homer, Collin G.; Kleinhesselink, Andrew R.; Adler, Peter B.

2016-01-01

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

Understanding climate impacts on recruitment and spatial dynamics of Atlantic cod in the Gulf of Maine: Integration of observations and modeling

NASA Astrophysics Data System (ADS)

Runge, Jeffrey A.; Kovach, Adrienne I.; Churchill, James H.; Kerr, Lisa A.; Morrison, John R.; Beardsley, Robert C.; Berlinsky, David L.; Chen, Changsheng; Cadrin, Steven X.; Davis, Cabell S.; Ford, Kathryn H.; Grabowski, Jonathan H.; Howell, W. Huntting; Ji, Rubao; Jones, Rebecca J.; Pershing, Andrew J.; Record, Nicholas R.; Thomas, Andrew C.; Sherwood, Graham D.; Tallack, Shelly M. L.; Townsend, David W.

2010-10-01

We put forward a combined observing and modeling strategy for evaluating effects of environmental forcing on the dynamics of spatially structured cod populations spawning in the western Gulf of Maine. Recent work indicates at least two genetically differentiated complexes in this region: a late spring spawning, coastal population centered in Ipswich Bay, and a population that spawns in winter inshore and on nearshore banks in the Gulf of Maine and off southern New England. The two populations likely differ in trophic interactions and in physiological and behavioral responses to different winter and spring environments. Coupled physical-biological modeling has advanced to the point where within-decade forecasting of environmental conditions for recruitment to each of the two populations is feasible. However, the modeling needs to be supported by hydrographic, primary production and zooplankton data collected by buoys, and by data from remote sensing and fixed station sampling. Forecasts of environmentally driven dispersal and growth of planktonic early life stages, combined with an understanding of possible population-specific predator fields, usage of coastal habitat by juveniles and adult resident and migratory patterns, can be used to develop scenarios for spatially explicit population responses to multiple forcings, including climate change, anthropogenic impacts on nearshore juvenile habitat, connectivity among populations and management interventions such as regional fisheries closures.

The psychology of intelligence analysis: drivers of prediction accuracy in world politics.

PubMed

Mellers, Barbara; Stone, Eric; Atanasov, Pavel; Rohrbaugh, Nick; Metz, S Emlen; Ungar, Lyle; Bishop, Michael M; Horowitz, Michael; Merkle, Ed; Tetlock, Philip

2015-03-01

This article extends psychological methods and concepts into a domain that is as profoundly consequential as it is poorly understood: intelligence analysis. We report findings from a geopolitical forecasting tournament that assessed the accuracy of more than 150,000 forecasts of 743 participants on 199 events occurring over 2 years. Participants were above average in intelligence and political knowledge relative to the general population. Individual differences in performance emerged, and forecasting skills were surprisingly consistent over time. Key predictors were (a) dispositional variables of cognitive ability, political knowledge, and open-mindedness; (b) situational variables of training in probabilistic reasoning and participation in collaborative teams that shared information and discussed rationales (Mellers, Ungar, et al., 2014); and (c) behavioral variables of deliberation time and frequency of belief updating. We developed a profile of the best forecasters; they were better at inductive reasoning, pattern detection, cognitive flexibility, and open-mindedness. They had greater understanding of geopolitics, training in probabilistic reasoning, and opportunities to succeed in cognitively enriched team environments. Last but not least, they viewed forecasting as a skill that required deliberate practice, sustained effort, and constant monitoring of current affairs. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Climate Forecasts and Water Resource Management: Applications for a Developing Country

NASA Astrophysics Data System (ADS)

Brown, C.; Rogers, P.

2002-05-01

While the quantity of water on the planet earth is relatively constant, the demand for water is continuously increasing. Population growth leads to linear increases in water demand, and economic growth leads to further demand growth. Strzepek et al. calculate that with a United Nations mean population estimate of 8.5 billion people by 2025 and globally balanced economic growth, water use could increase by 70% over that time (Strzepek et al., 1995). For developing nations especially, supplying water for this growing demand requires the construction of new water supply infrastructure. The prospect of designing and constructing long life-span infrastructure is clouded by the uncertainty of future climate. The availability of future water resources is highly dependent on future climate. With realization of the nonstationarity of climate, responsible design emphasizes resiliency and robustness of water resource systems (IPCC, 1995; Gleick et al., 1999). Resilient systems feature multiple sources and complex transport and distribution systems, and so come at a high economic and environmental price. A less capital-intense alternative to creating resilient and robust water resource systems is the use of seasonal climate forecasts. Such forecasts provide adequate lead time and accuracy to allow water managers and water-based sectors such as agriculture or hydropower to optimize decisions for the expected water supply. This study will assess the use of seasonal climate forecasts from regional climate models as a method to improve water resource management in systems with limited water supply infrastructure

Interrelations of green oak leaf roller population and common oak: results of 30-year monitoring and mathematical modeling

Treesearch

V. V. Rubtsov; I. A. Utkina

2003-01-01

Long-term monitoring followed by mathematical modeling was used to describe the population dynamics of the green oak leaf roller Tortrix viridana L. over a period of 30 years and to study reactions of oak stands to different levels of defoliation. The mathematical model allows us to forecast the population dynamics of the green oak leaf roller and...

The Eruption Forecasting Information System: Volcanic Eruption Forecasting Using Databases

NASA Astrophysics Data System (ADS)

Ogburn, S. E.; Harpel, C. J.; Pesicek, J. D.; Wellik, J.

2016-12-01

Forecasting eruptions, including the onset size, duration, location, and impacts, is vital for hazard assessment and risk mitigation. The Eruption Forecasting Information System (EFIS) project is a new initiative of the US Geological Survey-USAID Volcano Disaster Assistance Program (VDAP) and will advance VDAP's ability to forecast the outcome of volcanic unrest. The project supports probability estimation for eruption forecasting by creating databases useful for pattern recognition, identifying monitoring data thresholds beyond which eruptive probabilities increase, and for answering common forecasting questions. A major component of the project is a global relational database, which contains multiple modules designed to aid in the construction of probabilistic event trees and to answer common questions that arise during volcanic crises. The primary module contains chronologies of volcanic unrest. This module allows us to query eruption chronologies, monitoring data, descriptive information, operational data, and eruptive phases alongside other global databases, such as WOVOdat and the Global Volcanism Program. The EFIS database is in the early stages of development and population; thus, this contribution also is a request for feedback from the community. Preliminary data are already benefitting several research areas. For example, VDAP provided a forecast of the likely remaining eruption duration for Sinabung volcano, Indonesia, using global data taken from similar volcanoes in the DomeHaz database module, in combination with local monitoring time-series data. In addition, EFIS seismologists used a beta-statistic test and empirically-derived thresholds to identify distal volcano-tectonic earthquake anomalies preceding Alaska volcanic eruptions during 1990-2015 to retrospectively evaluate Alaska Volcano Observatory eruption precursors. This has identified important considerations for selecting analog volcanoes for global data analysis, such as differences between closed and open system volcanoes.

Signature-forecasting and early outbreak detection system

PubMed Central

Naumova, Elena N.; MacNeill, Ian B.

2008-01-01

SUMMARY Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a ‘signature’ curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

Forecasting volcanic unrest using seismicity: The good, the bad and the time consuming

NASA Astrophysics Data System (ADS)

Salvage, Rebecca; Neuberg, Jurgen W.

2013-04-01

Volcanic eruptions are inherently unpredictable in nature, with scientists struggling to forecast the type and timing of events, in particular in real time scenarios. Current understanding suggests that the use of statistical patterns within precursory datasets of seismicity prior to eruptive events could hold the potential to be used as real time forecasting tools. They allow us to determine times of clear deviation in data, which might be indicative of volcanic unrest. The identification of low frequency seismic swarms and the acceleration of this seismicity prior to observed volcanic unrest may be key in developing forecasting tools. The development of these real time forecasting models which can be implemented at volcano observatories is of particular importance since the identification of early warning signals allows danger to the proximal population to be minimized. We concentrate on understanding the significance and development of these seismic swarms as unrest develops at the volcano. In particular, analysis of accelerations in event rate, amplitude and energy rates released by seismicity prior to eruption suggests that these are important indicators of developing unrest. Real time analysis of these parameters simultaneously allows possible improvements to forecasting models. Although more time and computationally intense, cross correlation techniques applied to continuous seismicity prior to volcanic unrest scenarios allows all significant seismic events to be analysed, rather than only those which can be detected by an automated identification system. This may allow a more accurate forecast since all precursory seismicity can be taken into account. In addition, the classification of seismic events based on spectral characteristics may allow us to isolate individual types of signals which are responsible for certain types of unrest. In this way, we may be able to better forecast the type of eruption that may ensue, or at least some of its prevailing characteristics.

Past, Current, and Future Incidence Rates and Burden of Metastatic Prostate Cancer in the United States.

PubMed

Kelly, Scott P; Anderson, William F; Rosenberg, Philip S; Cook, Michael B

2017-11-18

Metastatic prostate cancer (PCA) remains a highly lethal malignancy in the USA. As prostate-specific antigen testing declines nationally, detailed assessment of current age- and race-specific incidence trends and quantitative forecasts are needed. To evaluate the current trends of metastatic PCA by age and race, and forecast the number of new cases (annual burden) and future trends. We derived incidence data for men aged ≥45 yr who were diagnosed with metastatic PCA from the population-based Surveillance, Epidemiology, and End Results registries. We examined the current trends of metastatic PCA from 2004 to 2014, and forecast the annual burden and incidence rates by age and race for 2015-2025, using age-period-cohort models and population projections. We also examined alternative forecasts (2012-2025) using trends prior to the revised screening guidelines issued in 2012. Metastatic PCA, steadily declining from 2004 to 2007 by 1.45%/yr, began to increase by 0.58%/yr after 2008, which accelerated to 2.74%/yr following the 2012 United States Preventive Services Task Force recommendations-a pattern that was magnified among men aged ≤69 yr and white men. Forecasts project the incidence to increase by 1.03%/yr through 2025, with men aged 45-54 yr (2.29%/yr) and 55-69 yr (1.53%/yr) increasing more rapidly. Meanwhile, the annual burden is expected to increase 42% by 2025. Our forecasts estimated an additional 15 891 metastatic cases from 2015 to 2025 compared with alternative forecasts using trends prior to 2012. The recent uptick in metastatic PCA rates has resulted in forecasts that project increasing rates through 2025, particularly among men aged ≤69 yr. Moreover, racial disparities are expected to persist and the annual burden will increase considerably. The impact of the prior and current PCA screening recommendations on metastatic PCA rates requires continued examination. In this report, we assessed how the incidence of metastatic prostate cancer has changed over recent years, and forecast future incidence trends and the number of new cases expected each year. We found that the incidence of metastatic prostate cancer has been increasing more rapidly since 2012, resulting in a rise in both future incidence and the number of new cases by 2025. Future incidence rates and the number of new cases were reduced in alternative forecasts using data prior to the 2012 United States Preventive Services Task Force (USPSTF) recommendations against prostate-specific antigen (PSA) testing for prostate cancer. There is a need for additional research that examines whether national declines in PSA testing contributed to increases in rates of metastatic disease. The incidence of metastatic disease in black men is still expected to occur at considerably higher rates compared with that in white men. Published by Elsevier B.V.

Development and application of a spatial IBM to forecast greater prairie-chicken population responses to land use in the Flint Hills region of Kansas

EPA Science Inventory

Greater prairie-chicken (Tympanachus cupido) populations have been on the decline for decades. Recent efforts to reverse this trend are focusing on two specific disturbance regimes, cattle grazing and field burning, both prevalent in the Flint Hill region of Kansas -- an area of...

The large area crop inventory experiment: A major demonstration of space remote sensing

NASA Technical Reports Server (NTRS)

Macdonald, R. B.; Hall, F. G.

1977-01-01

Strategies are presented in agricultural technology to increase the resistance of crops to a wider range of meteorological conditions in order to reduce year-to-year variations in crop production. Uncertainties in agricultral production, together with the consumer demands of an increasing world population, have greatly intensified the need for early and accurate annual global crop production forecasts. These forecasts must predict fluctuation with an accuracy, timeliness and known reliability sufficient to permit necessary social and economic adjustments, with as much advance warning as possible.

System development over the monitoring for the purpose of early warning of population from the threat of landslides. (Invited)

NASA Astrophysics Data System (ADS)

Zakhidova, D. V.; Kadyrhodjaev, A.; Scientific Team Of Hydroengeo Institute On Natural Hazards

2010-12-01

Well-timed warning of the population about possible landslide threat is one of the main positions in order to provide safe and stable country development. The system of monitoring over dangerous geological processes includes such components, as observation, forecast, control and management. Aspects of forecasting take special place. Having wide row of observations there can be possible to reveal some regularity of the phenomena, basing on which, it is possible to proceed forecasting. We looked through many approaches of forecasting that are used in different countries. The analysis of the available work has allowed to draw up a conclusion that while referring to the question of landslide forecasting, it is necessary to approach in system form, taking into account interacting components of the nature. The study of landslide processes has shown that these processes lies within the framework of engineering-geological directions of the science and also interacts with tectonics, geomorphology, hydrogeology, hydrology, climate change, technogenesis and etc. Thereby, the necessity of system approach, achievements of modern science and technology the most expedient approach to make a decision at landslide forecasting is probabilistic-statistical method with complex use of geological and satellite data, specific images processed through geoinformation systems. In this connection, probabilistic-statistical approach, reflecting natural characteristics of interacting natural system, allows to take into account multi-factored processes of landslide activations. Among the many factors, influencing on landslide activation, there exist ones that are not amenable to numerical feature. The parameters of these factors have descriptive, qualitative, rather than quantitative nature. Leaving these factors with lack of attention is absolutely not reasonable. Proposed approach has one more advantage, which allows taking into account not only numerical, but also non-numeric parameters. Combination of multidisciplinary, systematic feature, multifactorness of the account, probabilistic and statistical methods of the calculation, complex use of geological and satellite data, using modern technology processing and analysis of information - all these aspects were collected in one at proposed by authors approach to solve the question of defining the area of possible landslide activation. Proposed by authors method could be a part of the monitoring system for early warning of landslide activation. Thus, the authors propose to initialize the project “System development over the monitoring for the purpose of early warning of population from the threat of landslides”. In the process of project implementation there to be revealed such results like: 1. System of Geo-indicators in order to early warn quick-running landslide processes. 2. United interconnected system for remote, surface and underground types of observations over Geo-indicators. 3. Notification system of population about forthcoming threat by means of alerts, light signals, mobilization of municipalities and related ministries. In the result of project implementation there considered to reveal economic, technical, and social outputs.

Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which might provide better and more reliable forecast for the flood affected communities.

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.

PubMed

Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A

2014-08-01

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions. © 2014 John Wiley & Sons Ltd.

An Operational System for Surveillance and Ecological Forecasting of West Nile Virus Outbreaks

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Davis, J. K.; Vincent, G.; Hess, A.; Hildreth, M. B.

2017-12-01

Mosquito-borne disease surveillance has traditionally focused on tracking human cases along with the abundance and infection status of mosquito vectors. For many of these diseases, vector and host population dynamics are also sensitive to climatic factors, including temperature fluctuations and the availability of surface water for mosquito breeding. Thus, there is a potential to strengthen surveillance and predict future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites. The South Dakota Mosquito Information System (SDMIS) project combines entomological surveillance with gridded meteorological data from NASA's North American Land Data Assimilation System (NLDAS) to generate weekly risk maps for West Nile virus (WNV) in the north-central United States. Critical components include a mosquito infection model that smooths the noisy infection rate and compensates for unbalanced sampling, and a human infection model that combines the entomological risk estimates with lagged effects of meteorological variables from the North American Land Data Assimilation System (NLDAS). Two types of forecasts are generated: long-term forecasts of statewide risk extending through the entire WNV season, and short-term forecasts of the geographic pattern of WNV risk in the upcoming week. Model forecasts are connected to public health actions through decision support matrices that link predicted risk levels to a set of phased responses. In 2016, the SDMIS successfully forecast an early start to the WNV season and a large outbreak of WNV cases following several years of low transmission. An evaluation of the 2017 forecasts will also be presented. Our experiences with the SDMIS highlight several important lessons that can inform future efforts at disease early warning. These include the value of integrating climatic models with recent observations of infection, the critical role of automated workflows to facilitate the timely integration of multiple data streams, the need for effective synthesis and visualization of forecasts, and the importance of linking forecasts to specific public health responses.

Forecasting and evaluating patterns of energy development in southwestern Wyoming

USGS Publications Warehouse

Garman, Steven L.

2015-01-01

The effects of future oil and natural gas development in southwestern Wyoming on wildlife populations are topical to conservation of the sagebrush steppe ecosystem. To aid in understanding these potential effects, the U.S. Geological Survey developed an Energy Footprint simulation model that forecasts the amount and pattern of energy development under different assumptions of development rates and well-drilling methods. The simulated disturbance patterns produced by the footprint model are used to assess the potential effects on wildlife habitat and populations. A goal of this modeling effort is to use measures of energy production (number of simulated wells), well-pad and road-surface disturbance, and potential effects on wildlife to identify build-out designs that minimize the physical and ecological footprint of energy development for different levels of energy production and development costs.

Projecting surgeon supply using a dynamic model.

PubMed

Fraher, Erin P; Knapton, Andy; Sheldon, George F; Meyer, Anthony; Ricketts, Thomas C

2013-05-01

To develop a projection model to forecast the head count and full-time equivalent supply of surgeons by age, sex, and specialty in the United States from 2009 to 2028. The search for the optimal number and specialty mix of surgeons to care for the United States population has taken on increased urgency under health care reform. Expanded insurance coverage and an aging population will increase demand for surgical and other medical services. Accurate forecasts of surgical service capacity are crucial to inform the federal government, training institutions, professional associations, and others charged with improving access to health care. The study uses a dynamic stock and flow model that simulates future changes in numbers and specialty type by factoring in changes in surgeon demographics and policy factors. : Forecasts show that overall surgeon supply will decrease 18% during the period form 2009 to 2028 with declines in all specialties except colorectal, pediatric, neurological surgery, and vascular surgery. Model simulations suggest that none of the proposed changes to increase graduate medical education currently under consideration will be sufficient to offset declines. The length of time it takes to train surgeons, the anticipated decrease in hours worked by surgeons in younger generations, and the potential decreases in graduate medical education funding suggest that there may be an insufficient surgeon workforce to meet population needs. Existing maldistribution patterns are likely to be exacerbated, leading to delayed or lost access to time-sensitive surgical procedures, particularly in rural areas.

Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

PubMed

Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

2013-02-01

Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model.

PubMed

Mao, Qiang; Zhang, Kai; Yan, Wu; Cheng, Chaonan

2018-05-02

The aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources. Data for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box-Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months. During the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1) 12 , which demonstrated adequate information extraction (white noise test, p>0.05). Based on the analysis, the incidence of TB from January to June 2016 were 6.6335, 4.7208, 5.8193, 5.5474, 5.2202 and 4.9156 per 100,000 of the population, respectively. According to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Forecasting Ontario's blood supply and demand.

PubMed

Drackley, Adam; Newbold, K Bruce; Paez, Antonio; Heddle, Nancy

2012-02-01

Given an aging population that requires increased medical care, an increasing number of deferrals from the donor pool, and a growing immigrant population that typically has lower donation rates, the purpose of this article is to forecast Ontario's blood supply and demand. We calculate age- and sex-specific donation and demand rates for blood supply based on 2008 data and project demand between 2008 and 2036 based on these rates and using population data from the Ontario Ministry of Finance. Results indicate that blood demand will outpace supply as early as 2012. For instance, while the total number of donations made by older cohorts is expected to increase in the coming years, the number of red blood cell (RBC) transfusions in the 70+ age group is forecasted grow from approximately 53% of all RBC transfusions in 2008 (209,515) in 2008 to 68% (546,996) by 2036. A series of alternate scenarios, including projections based on a 2% increase in supply per year and increased use of apheresis technology, delays supply shortfalls, but does not eliminate them without active management and/or multiple methods to increase supply and decrease demand. Predictions show that demand for blood products will outpace supply in the near future given current age- and sex-specific supply and demand rates. However, we note that the careful management of the blood supply by Canadian Blood Services, along with new medical techniques and the recruitment of new donors to the system, will remove future concerns. © 2012 American Association of Blood Banks.

Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting.

PubMed

LaDeau, Shannon L; Glass, Gregory E; Hobbs, N Thompson; Latimer, Andrew; Ostfeld, Richard S

2011-07-01

Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854. Still, zoonotic and vector-borne diseases increasingly affect human populations, and methods used to successfully characterize directly transmitted diseases are often insufficient. We use four case studies to demonstrate that advances in disease forecasting require better understanding of zoonotic host and vector populations, as well of the dynamics that facilitate pathogen amplification and disease spillover into humans. In each case study, this goal is complicated by limited data, spatiotemporal variability in pathogen transmission and impact, and often, insufficient biological understanding. We present a conceptual framework for data-model fusion in infectious disease research that addresses these fundamental challenges using a hierarchical state-space structure to (1) integrate multiple data sources and spatial scales to inform latent parameters, (2) partition uncertainty in process and observation models, and (3) explicitly build upon existing ecological and epidemiological understanding. Given the constraints inherent in the study of infectious disease and the urgent need for progress, fusion of data and expertise via this type of conceptual framework should prove an indispensable tool.

Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

NASA Astrophysics Data System (ADS)

Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

2016-10-01

Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

PubMed Central

Shi, Yuan; Liu, Xu; Kok, Suet-Yheng; Rajarethinam, Jayanthi; Liang, Shaohong; Yap, Grace; Chong, Chee-Seng; Lee, Kim-Sung; Tan, Sharon S.Y.; Chin, Christopher Kuan Yew; Lo, Andrew; Kong, Waiming; Ng, Lee Ching; Cook, Alex R.

2015-01-01

Background: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. Objectives: We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. Methods: We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Results: Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Conclusions: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Citation: Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369–1375; http://dx.doi.org/10.1289/ehp.1509981 PMID:26662617

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Zappa, M.

2011-01-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This models chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that COSMO-LEPS-based hydrological forecasts overall outperform their COSMO-7 based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts and used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

NASA Technical Reports Server (NTRS)

Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt

2009-01-01

The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

Development of personal pollen information—the next generation of pollen information and a step forward for hay fever sufferers

NASA Astrophysics Data System (ADS)

Kmenta, Maximilian; Bastl, Katharina; Jäger, Siegfried; Berger, Uwe

2014-10-01

Pollen allergies affect a large part of the European population and are considered likely to increase. User feedback indicates that there are difficulties in providing proper information and valid forecasts using traditional methods of aerobiology due to a variety of factors. Allergen content, pollen loads, and pollen allergy symptoms vary per region and year. The first steps in challenging such issues have already been undertaken. A personalized pollen-related symptom forecast is thought to be a possible answer. However, attempts made thus far have not led to an improvement in daily forecasting procedures. This study describes a model that was launched in 2013 in Austria to provide the first available personal pollen information. This system includes innovative forecast models using bi-hourly pollen data, traditional pollen forecasts based on historical data, meteorological data, and recent symptom data from the patient's hayfever diary. Furthermore, it calculates the personal symptom load in real time, in particular, the entries of the previous 5 days, to classify users. The personal pollen information was made available in Austria on the Austrian pollen information website and via a mobile pollen application, described herein for the first time. It is supposed that the inclusion of personal symptoms will lead to major improvements in pollen information concerning hay fever sufferers.

Application of Jason-2/3 Altimetry for Virtual Gauging and Flood Forecasting in Mekong Basin

NASA Astrophysics Data System (ADS)

Lee, H.; Hossain, F.; Okeowo, M. A.; Nguyen, L. D.; Bui, D. D.; Chang, C. H.

2016-12-01

Vietnam suffers from both flood and drought during the rainy and dry seasons, respectively, due to its highly varying surface water resources. However, the National Center for Water Resources Planning and Investigation (NAWAPI) states that only 7 surface water monitoring stations have been constructed in Central and Highland Central regions with 100 station planned to be constructed by 2030 throughout Vietnam. For the Mekong Delta (MD), the Mekong River Commission (MRC) provides 7-day river level forecasting, but only at the two gauge stations located near the border between Cambodia and Vietnam (http://ffw.mrcmekong.org/south.htm). In order to help stakeholder agencies monitor upstream processes in the rivers and manage their impacts on the agricultural sector and densely populated delta cities, we, first of all, construct the so-called virtual stations throughout the entire Mekong River using the fully automated river level extraction tool with Jason-2/3 Geophysical Research Record (GDR) data. Then, we discuss the potentials and challenges of river level forecasting using Jason-2/3 Interim GDR (IGDR) data, which has 1 - 2 days of latency, over the Mekong River. Finally, based on our analyses, we propose a forecasting system for the Mekong River by drawing from our experience in operationalizing Jason-2 altimetry for Bangladesh flood forecasting.

Optimal Scaling of Aftershock Zones using Ground Motion Forecasts

NASA Astrophysics Data System (ADS)

Wilson, John Max; Yoder, Mark R.; Rundle, John B.

2018-02-01

The spatial distribution of aftershocks following major earthquakes has received significant attention due to the shaking hazard these events pose for structures and populations in the affected region. Forecasting the spatial distribution of aftershock events is an important part of the estimation of future seismic hazard. A simple spatial shape for the zone of activity has often been assumed in the form of an ellipse having semimajor axis to semiminor axis ratio of 2.0. However, since an important application of these calculations is the estimation of ground shaking hazard, an effective criterion for forecasting future aftershock impacts is to use ground motion prediction equations (GMPEs) in addition to the more usual approach of using epicentral or hypocentral locations. Based on these ideas, we present an aftershock model that uses self-similarity and scaling relations to constrain parameters as an option for such hazard assessment. We fit the spatial aspect ratio to previous earthquake sequences in the studied regions, and demonstrate the effect of the fitting on the likelihood of post-disaster ground motion forecasts for eighteen recent large earthquakes. We find that the forecasts in most geographic regions studied benefit from this optimization technique, while some are better suited to the use of the a priori aspect ratio.

Updating the transportation plans in Virginia's small urban areas.

DOT National Transportation Integrated Search

1987-01-01

The Transportation Planning Division (TPD) of the Virginia Department of Transportation is responsible for developing transportation plans for areas in the state having a population greater than 3,500. Although transportation forecasting procedures f...

Census mapbook for transportation planning.

DOT National Transportation Integrated Search

1994-12-01

Geographic display of Census data in transportation planning and policy decisions are compiled in a report of 49 maps, depicting use of the data in applications such as travel demand model development and model validation, population forecasting, cor...

Physician supply forecast: better than peering in a crystal ball?

PubMed Central

Roberfroid, Dominique; Leonard, Christian; Stordeur, Sabine

2009-01-01

Background Anticipating physician supply to tackle future health challenges is a crucial but complex task for policy planners. A number of forecasting tools are available, but the methods, advantages and shortcomings of such tools are not straightforward and not always well appraised. Therefore this paper had two objectives: to present a typology of existing forecasting approaches and to analyse the methodology-related issues. Methods A literature review was carried out in electronic databases Medline-Ovid, Embase and ERIC. Concrete examples of planning experiences in various countries were analysed. Results Four main forecasting approaches were identified. The supply projection approach defines the necessary inflow to maintain or to reach in the future an arbitrary predefined level of service offer. The demand-based approach estimates the quantity of health care services used by the population in the future to project physician requirements. The needs-based approach involves defining and predicting health care deficits so that they can be addressed by an adequate workforce. Benchmarking health systems with similar populations and health profiles is the last approach. These different methods can be combined to perform a gap analysis. The methodological challenges of such projections are numerous: most often static models are used and their uncertainty is not assessed; valid and comprehensive data to feed into the models are often lacking; and a rapidly evolving environment affects the likelihood of projection scenarios. As a result, the internal and external validity of the projections included in our review appeared limited. Conclusion There is no single accepted approach to forecasting physician requirements. The value of projections lies in their utility in identifying the current and emerging trends to which policy-makers need to respond. A genuine gap analysis, an effective monitoring of key parameters and comprehensive workforce planning are key elements to improving the usefulness of physician supply projections. PMID:19216772

Transforming Atmospheric and Remotely-Sensed Information to Hydrologic Predictability in South Asia

NASA Astrophysics Data System (ADS)

Hopson, T. M.; Riddle, E. E.; Broman, D.; Brakenridge, G. R.; Birkett, C. M.; Kettner, A.; Sampson, K. M.; Boehnert, J.; Priya, S.; Collins, D. C.; Rostkier-Edelstein, D.; Young, W.; Singh, D.; Islam, A. S.

2017-12-01

South Asia is a flashpoint for natural disasters with profound societal impacts for the region and globally. Although close to 40% of the world's population depends on the Greater Himalaya's great rivers, $20 Billion of GDP is affected by river floods each year. The frequent occurrence of floods, combined with large and rapidly growing populations with high levels of poverty, make South Asia highly susceptible to humanitarian disasters. The challenges of mitigating such devastating disasters are exacerbated by the limited availability of real-time rain and stream gauge measuring stations and transboundary data sharing, and by constrained institutional commitments to overcome these challenges. To overcome such limitations, India and the World Bank have committed resources to the National Hydrology Project III, with the development objective to improve the extent, quality, and accessibility of water resources information and to strengthen the capacity of targeted water resources management institutions in India. The availability and application of remote sensing products and weather forecasts from ensemble prediction systems (EPS) have transformed river forecasting capability over the last decade, and is of interest to India. In this talk, we review the potential predictability of river flow contributed by some of the freely-available remotely-sensed and weather forecasting products within the framework of the physics of water migration through a watershed. Our specific geographical context is the Ganges, Brahmaputra, and Meghna river basin and a newly-available set of stream gauge measurements located over the region. We focus on satellite rainfall estimation, river height and width estimation, and EPS weather forecasts. For the later, we utilize the THORPEX-TIGGE dataset of global forecasts, and discuss how atmospheric predictability, as measured by an EPS, is transformed into hydrometeorological predictability. We provide an overview of the strengths and weaknesses of each of these data sets to the river flow prediction problem, generalizing their utility across spatial- and temporal-scales, and highlight the benefits of joint utilization and multi-modeling to minimize uncertainty and enhance operational robustness.

Development and assessment of 30-meter pine density maps for landscape-level modeling of mountain pine beetle dynamics

Treesearch

Benjamin A. Crabb; James A. Powell; Barbara J. Bentz

2012-01-01

Forecasting spatial patterns of mountain pine beetle (MPB) population success requires spatially explicit information on host pine distribution. We developed a means of producing spatially explicit datasets of pine density at 30-m resolution using existing geospatial datasets of vegetation composition and structure. Because our ultimate goal is to model MPB population...

Real-Time Population Health Detector

DTIC Science & Technology

2004-11-01

military and civilian populations. General Dynamics (then Veridian Systems Division), in cooperation with Stanford University, won a competitive DARPA...via the sequence of one-step ahead forecast errors from the Kalman recursions: 1| −−= tttt Hye µ The log-likelihood then follows by treating the... parking in the transient parking structure. Norfolk Area Military Treatment Facility Patient Files GDAIS received historic CHCS data from all

Development and application of a spatial IBM to forecast greater prairie-chicken population responses to land use in the Flint Hills region of Kansas - SCB meeting

EPA Science Inventory

Greater prairie-chicken (Tympanachus cupido) populations have been on the decline for decades. Recent efforts to reverse this trend are focusing on two specific disturbance regimes, cattle grazing and field burning, both prevalent in the Flint Hill region of Kansas -- an area of...

Ice and AIS: ship speed data and sea ice forecasts in the Baltic Sea

NASA Astrophysics Data System (ADS)

Löptien, U.; Axell, L.

2014-12-01

The Baltic Sea is a seasonally ice-covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, the forecast comprises several ice properties that are distributed as prognostic variables, but their actual usefulness is difficult to measure, and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the automatic identification system (AIS), with the respective forecasted ice conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62-67% of ship speed variations can be explained by the forecasted ice properties when fitting a mixed-effect model. This statistical fit is based on a test region in the Bothnian Sea during the severe winter 2011 and employs 15 to 25 min averages of ship speed.

The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric

2018-04-01

A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

Forecasting Social Unrest Using Activity Cascades

PubMed Central

Cadena, Jose; Korkmaz, Gizem; Kuhlman, Chris J.; Marathe, Achla; Ramakrishnan, Naren; Vullikanti, Anil

2015-01-01

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen “on the ground.” Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach. PMID:26091012

Forecasting Social Unrest Using Activity Cascades.

PubMed

Cadena, Jose; Korkmaz, Gizem; Kuhlman, Chris J; Marathe, Achla; Ramakrishnan, Naren; Vullikanti, Anil

2015-01-01

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen "on the ground." Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.

Obesity and severe obesity forecasts through 2030.

PubMed

Finkelstein, Eric A; Khavjou, Olga A; Thompson, Hope; Trogdon, Justin G; Pan, Liping; Sherry, Bettylou; Dietz, William

2012-06-01

Previous efforts to forecast future trends in obesity applied linear forecasts assuming that the rise in obesity would continue unabated. However, evidence suggests that obesity prevalence may be leveling off. This study presents estimates of adult obesity and severe obesity prevalence through 2030 based on nonlinear regression models. The forecasted results are then used to simulate the savings that could be achieved through modestly successful obesity prevention efforts. The study was conducted in 2009-2010 and used data from the 1990 through 2008 Behavioral Risk Factor Surveillance System (BRFSS). The analysis sample included nonpregnant adults aged ≥ 18 years. The individual-level BRFSS variables were supplemented with state-level variables from the U.S. Bureau of Labor Statistics, the American Chamber of Commerce Research Association, and the Census of Retail Trade. Future obesity and severe obesity prevalence were estimated through regression modeling by projecting trends in explanatory variables expected to influence obesity prevalence. Linear time trend forecasts suggest that by 2030, 51% of the population will be obese. The model estimates a much lower obesity prevalence of 42% and severe obesity prevalence of 11%. If obesity were to remain at 2010 levels, the combined savings in medical expenditures over the next 2 decades would be $549.5 billion. The study estimates a 33% increase in obesity prevalence and a 130% increase in severe obesity prevalence over the next 2 decades. If these forecasts prove accurate, this will further hinder efforts for healthcare cost containment. Copyright © 2012 Elsevier Inc. All rights reserved.

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data.

PubMed

Moss, Robert; Zarebski, Alexander; Dawson, Peter; McCaw, James M

2016-07-01

Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, as these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, using existing surveillance data, but these methods must be tailored both to the target population and to the surveillance system. Our aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia). We used the bootstrap particle filter and a mechanistic infection model to generate epidemic forecasts for metropolitan Melbourne (Australia) from weekly Internet search query surveillance data reported by Google Flu Trends for 2006-14. Optimal observation models were selected from hundreds of candidates using a novel approach that treats forecasts akin to receiver operating characteristic (ROC) curves. We show that the timing of the epidemic peak can be accurately predicted 4-6 weeks in advance, but that the magnitude of the epidemic peak and the overall burden are much harder to predict. We then discuss how the infection and observation models and the filtering process may be refined to improve forecast robustness, thereby improving the utility of these methods for healthcare decision support. © 2016 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.

Past and projected trends of body mass index and weight status in South Australia: 2003 to 2019.

PubMed

Hendrie, Gilly A; Ullah, Shahid; Scott, Jane A; Gray, John; Berry, Narelle; Booth, Sue; Carter, Patricia; Cobiac, Lynne; Coveney, John

2015-12-01

Functional data analysis (FDA) is a forecasting approach that, to date, has not been applied to obesity, and that may provide more accurate forecasting analysis to manage uncertainty in public health. This paper uses FDA to provide projections of Body Mass Index (BMI), overweight and obesity in an Australian population through to 2019. Data from the South Australian Monitoring and Surveillance System (January 2003 to December 2012, n=51,618 adults) were collected via telephone interview survey. FDA was conducted in four steps: 1) age-gender specific BMIs for each year were smoothed using a weighted regression; 2) the functional principal components decomposition was applied to estimate the basis functions; 3) an exponential smoothing state space model was used for forecasting the coefficient series; and 4) forecast coefficients were combined with the basis function. The forecast models suggest that between 2012 and 2019 average BMI will increase from 27.2 kg/m(2) to 28.0 kg/m(2) in males and 26.4 kg/m(2) to 27.6 kg/m(2) in females. The prevalence of obesity is forecast to increase by 6-7 percentage points by 2019 (to 28.7% in males and 29.2% in females). Projections identify age-gender groups at greatest risk of obesity over time. The novel approach will be useful to facilitate more accurate planning and policy development. © 2015 Public Health Association of Australia.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2011-07-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that overall COSMO-LEPS-based hydrological forecasts outperforms their COSMO-7-based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts, and are used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment. No definitive conclusion on the model chain capacity to forecast flooding events endangering the city of Zurich could be drawn because of the under-sampling of extreme events. Further research on the form of the reforecasts needed to infer on floods associated to return periods of several decades, centuries, is encouraged.

A novel visualisation tool for climate services: a case study of temperature extremes and human mortality in Europe

NASA Astrophysics Data System (ADS)

Lowe, R.; Ballester, J.; Robine, J.; Herrmann, F. R.; Jupp, T. E.; Stephenson, D.; Rodó, X.

2013-12-01

Users of climate information often require probabilistic information on which to base their decisions. However, communicating information contained within a probabilistic forecast presents a challenge. In this paper we demonstrate a novel visualisation technique to display ternary probabilistic forecasts on a map in order to inform decision making. In this method, ternary probabilistic forecasts, which assign probabilities to a set of three outcomes (e.g. low, medium, and high risk), are considered as a point in a triangle of barycentric coordinates. This allows a unique colour to be assigned to each forecast from a continuum of colours defined on the triangle. Colour saturation increases with information gain relative to the reference forecast (i.e. the long term average). This provides additional information to decision makers compared with conventional methods used in seasonal climate forecasting, where one colour is used to represent one forecast category on a forecast map (e.g. red = ';dry'). We use the tool to present climate-related mortality projections across Europe. Temperature and humidity are related to human mortality via location-specific transfer functions, calculated using historical data. Daily mortality data at the NUTS2 level for 16 countries in Europe were obtain from 1998-2005. Transfer functions were calculated for 54 aggregations in Europe, defined using criteria related to population and climatological similarities. Aggregations are restricted to fall within political boundaries to avoid problems related to varying adaptation policies between countries. A statistical model is fit to cold and warm tails to estimate future mortality using forecast temperatures, in a Bayesian probabilistic framework. Using predefined categories of temperature-related mortality risk, we present maps of probabilistic projections for human mortality at seasonal to decadal time scales. We demonstrate the information gained from using this technique compared to more traditional methods to display ternary probabilistic forecasts. This technique allows decision makers to identify areas where the model predicts with certainty area-specific heat waves or cold snaps, in order to effectively target resources to those areas most at risk, for a given season or year. It is hoped that this visualisation tool will facilitate the interpretation of the probabilistic forecasts not only for public health decision makers but also within a multi-sectoral climate service framework.

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

2014-01-01

 The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

Prediction of kharif rice yield at Kharagpur using disaggregated extended range rainfall forecasts

NASA Astrophysics Data System (ADS)

Dhekale, B. S.; Nageswararao, M. M.; Nair, Archana; Mohanty, U. C.; Swain, D. K.; Singh, K. K.; Arunbabu, T.

2017-08-01

The Extended Range Forecasts System (ERFS) has been generating monthly and seasonal forecasts on real-time basis throughout the year over India since 2009. India is one of the major rice producer and consumer in South Asia; more than 50% of the Indian population depends on rice as staple food. Rice is mainly grown in kharif season, which contributed 84% of the total annual rice production of the country. Rice cultivation in India is rainfed, which depends largely on rains, so reliability of the rainfall forecast plays a crucial role for planning the kharif rice crop. In the present study, an attempt has been made to test the reliability of seasonal and sub-seasonal ERFS summer monsoon rainfall forecasts for kharif rice yield predictions at Kharagpur, West Bengal by using CERES-Rice (DSSATv4.5) model. These ERFS forecasts are produced as monthly and seasonal mean values and are converted into daily sequences with stochastic weather generators for use with crop growth models. The daily sequences are generated from ERFS seasonal (June-September) and sub-seasonal (July-September, August-September, and September) summer monsoon (June to September) rainfall forecasts which are considered as input in CERES-rice crop simulation model for the crop yield prediction for hindcast (1985-2008) and real-time mode (2009-2015). The yield simulated using India Meteorological Department (IMD) observed daily rainfall data is considered as baseline yield for evaluating the performance of predicted yields using the ERFS forecasts. The findings revealed that the stochastic disaggregation can be used to disaggregate the monthly/seasonal ERFS forecasts into daily sequences. The year to year variability in rice yield at Kharagpur is efficiently predicted by using the ERFS forecast products in hindcast as well as real time, and significant enhancement in the prediction skill is noticed with advancement in the season due to incorporation of observed weather data which reduces uncertainty of yield prediction. The findings also recommend that the normal and above normal yields are predicted well in advance using the ERFS forecasts. The outcomes of this study are useful to farmers for taking appropriate decisions well in advance for climate risk management in rice production during different stages of the crop growing season at Kharagpur.

Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

NASA Astrophysics Data System (ADS)

Mukkavilli, S. K.; Kay, M. J.; Taylor, R.; Prasad, A. A.; Troccoli, A.

2014-12-01

The Australian Solar Energy Forecasting System (ASEFS) project requires forecasting timeframes which range from nowcasting to long-term forecasts (minutes to two years). As concentrating solar power (CSP) plant operators are one of the key stakeholders in the national energy market, research and development enhancements for direct normal irradiance (DNI) forecasts is a major subtask. This project involves comparing different radiative scheme codes to improve day ahead DNI forecasts on the national supercomputing infrastructure running mesoscale simulations on NOAA's Weather Research & Forecast (WRF) model. ASEFS also requires aerosol data fusion for improving accurate representation of spatio-temporally variable atmospheric aerosols to reduce DNI bias error in clear sky conditions over southern Queensland & New South Wales where solar power is vulnerable to uncertainities from frequent aerosol radiative events such as bush fires and desert dust. Initial results from thirteen years of Bureau of Meteorology's (BOM) deseasonalised DNI and MODIS NASA-Terra aerosol optical depth (AOD) anomalies demonstrated strong negative correlations in north and southeast Australia along with strong variability in AOD (~0.03-0.05). Radiative transfer schemes, DNI and AOD anomaly correlations will be discussed for the population and transmission grid centric regions where current and planned CSP plants dispatch electricity to capture peak prices in the market. Aerosol and solar irradiance datasets include satellite and ground based assimilations from the national BOM, regional aerosol researchers and agencies. The presentation will provide an overview of this ASEFS project task on WRF and results to date. The overall goal of this ASEFS subtask is to develop a hybrid numerical weather prediction (NWP) and statistical/machine learning multi-model ensemble strategy that meets future operational requirements of CSP plant operators.

Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management

NASA Astrophysics Data System (ADS)

Kleindinst, Judith L.; Anderson, Donald M.; McGillicuddy, Dennis J.; Stumpf, Richard P.; Fisher, Kathleen M.; Couture, Darcie A.; Michael Hickey, J.; Nash, Christopher

2014-05-01

Development of forecasting systems for harmful algal blooms (HABs) has been a long-standing research and management goal. Significant progress has been made in the Gulf of Maine, where seasonal bloom forecasts are now being issued annually using Alexandrium fundyense cyst abundance maps and a population dynamics model developed for that organism. Thus far, these forecasts have used terms such as “significant”, “moderately large” or “moderate” to convey the extent of forecasted paralytic shellfish poisoning (PSP) outbreaks. In this study, historical shellfish harvesting closure data along the coast of the Gulf of Maine were used to derive a series of bloom severity levels that are analogous to those used to define major storms like hurricanes or tornados. Thirty-four years of PSP-related shellfish closure data for Maine, Massachusetts and New Hampshire were collected and mapped to depict the extent of coastline closure in each year. Due to fractal considerations, different methods were explored for measuring length of coastline closed. Ultimately, a simple procedure was developed using arbitrary straight-line segments to represent specific sections of the coastline. This method was consistently applied to each year’s PSP toxicity closure map to calculate the total length of coastline closed. Maps were then clustered together statistically to yield distinct groups of years with similar characteristics. A series of categories or levels was defined (“Level 1: Limited”, “Level 2: Moderate”, and “Level 3: Extensive”) each with an associated range of expected coastline closed, which can now be used instead of vague descriptors in future forecasts. This will provide scientifically consistent and simply defined information to the public as well as resource managers who make decisions on the basis of the forecasts.

Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management

PubMed Central

Kleindinst, Judith L.; Anderson, Donald M.; McGillicuddy, Dennis J.; Stumpf, Richard P.; Fisher, Kathleen M.; Couture, Darcie A.; Hickey, J. Michael; Nash, Christopher

2014-01-01

Development of forecasting systems for harmful algal blooms (HABs) has been a long-standing research and management goal. Significant progress has been made in the Gulf of Maine, where seasonal bloom forecasts are now being issued annually using Alexandrium fundyense cyst abundance maps and a population dynamics model developed for that organism. Thus far, these forecasts have used terms such as “significant”, “moderately large” or “moderate” to convey the extent of forecasted paralytic shellfish poisoning (PSP) outbreaks. In this study, historical shellfish harvesting closure data along the coast of the Gulf of Maine were used to derive a series of bloom severity levels that are analogous to those used to define major storms like hurricanes or tornados. Thirty-four years of PSP-related shellfish closure data for Maine, Massachusetts and New Hampshire were collected and mapped to depict the extent of coastline closure in each year. Due to fractal considerations, different methods were explored for measuring length of coastline closed. Ultimately, a simple procedure was developed using arbitrary straight-line segments to represent specific sections of the coastline. This method was consistently applied to each year’s PSP toxicity closure map to calculate the total length of coastline closed. Maps were then clustered together statistically to yield distinct groups of years with similar characteristics. A series of categories or levels was defined (“Level 1: Limited”, “Level 2: Moderate”, and “Level 3: Extensive”) each with an associated range of expected coastline closed, which can now be used instead of vague descriptors in future forecasts. This will provide scientifically consistent and simply defined information to the public as well as resource managers who make decisions on the basis of the forecasts. PMID:25076815

Linked population and economic models: some methodological issues in forecasting, analysis, and policy optimization.

PubMed

Madden, M; Batey Pwj

1983-05-01

Some problems associated with demographic-economic forecasting include finding models appropriate for a declining economy with unemployment, using a multiregional approach in an interregional model, finding a way to show differential consumption while endogenizing unemployment, and avoiding unemployment inconsistencies. The solution to these problems involves the construction of an activity-commodity framework, locating it within a group of forecasting models, and indicating possible ratios towards dynamization of the framework. The authors demonstrate the range of impact multipliers that can be derived from the framework and show how these multipliers relate to Leontief input-output multipliers. It is shown that desired population distribution may be obtained by selecting instruments from the economic sphere to produce, through the constraints vector of an activity-commodity framework, targets selected from demographic activities. The next step in this process, empirical exploitation, was carried out by the authors in the United Kingdom, linking an input-output model with a wide selection of demographic and demographic-economic variables. The generally tenuous control which government has over any variables in systems of this type, especially in market economies, makes application in the policy field of the optimization approach a partly conjectural exercise, although the analytic capacity of the approach can provide clear indications of policy directions.

Improved management of small pelagic fisheries through seasonal climate prediction.

PubMed

Tommasi, Désirée; Stock, Charles A; Pegion, Kathleen; Vecchi, Gabriel A; Methot, Richard D; Alexander, Michael A; Checkley, David M

2017-03-01

Populations of small pelagic fish are strongly influenced by climate. The inability of managers to anticipate environment-driven fluctuations in stock productivity or distribution can lead to overfishing and stock collapses, inflexible management regulations inducing shifts in the functional response to human predators, lost opportunities to harvest populations, bankruptcies in the fishing industry, and loss of resilience in the human food supply. Recent advances in dynamical global climate prediction systems allow for sea surface temperature (SST) anomaly predictions at a seasonal scale over many shelf ecosystems. Here we assess the utility of SST predictions at this "fishery relevant" scale to inform management, using Pacific sardine as a case study. The value of SST anomaly predictions to management was quantified under four harvest guidelines (HGs) differing in their level of integration of SST data and predictions. The HG that incorporated stock biomass forecasts informed by skillful SST predictions led to increases in stock biomass and yield, and reductions in the probability of yield and biomass falling below socioeconomic or ecologically acceptable levels. However, to mitigate the risk of collapse in the event of an erroneous forecast, it was important to combine such forecast-informed harvest controls with additional harvest restrictions at low biomass. © 2016 by the Ecological Society of America.

National Transportation Noise Mapping Tool

DOT National Transportation Integrated Search

2017-03-01

By most forecasts, the U.S. population is projected to grow by over 100 million by 2050. As demand for transportation increases, transportation-related noise will also change. The Bureau of Transportation Statistics (BTS) has started a national, mult...

Studies in short haul air transportation in the California corridor: Effects of design runway length; community acceptance; impact of return on investment and fuel cost increases. Volume 2: Appendices

NASA Technical Reports Server (NTRS)

Shevell, R. S.; Jones, D. W., Jr.

1973-01-01

The development of a forecast model for short haul air transportation systems in the California Corridor is discussed. The factors which determine the level of air traffic demand are identified. A forecast equation for use in airport utilization analysis is developed. A mathematical model is submitted to show the relationship between population, employment, and income for indicating future air transportation utilization. Diagrams and tables of data are included to support the conclusions reached regarding air transportation economic factors.

Benefits of volcano monitoring far outweigh costs - the case of Mount Pinatubo

USGS Publications Warehouse

Newhall, Chris G.; Hendley, James W.; Stauffer, Peter H.

1997-01-01

The climactic June 1991 eruption of Mount Pinatubo, Philippines, was the largest volcanic eruption in this century to affect a heavily populated area. Because it was forecast by scientists from the Philippine Institute of Volcanology and Seismology and the U.S. Geological Survey, civil and military leaders were able to order massive evacuations and take measures to protect property before the eruption. Thousands of lives were saved and hundreds of millions of dollars in property losses averted. The savings in property alone were many times the total costs of the forecasting and evacuations.

Replacing climatological potential evapotranspiration estimates with dynamic satellite-based observations in operational hydrologic prediction models

NASA Astrophysics Data System (ADS)

Franz, K. J.; Bowman, A. L.; Hogue, T. S.; Kim, J.; Spies, R.

2011-12-01

In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Current operational forecasting utilizes watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature and climatological potential evapotranspiration (PET) estimates. The PET values are derived from historic pan evaporation observations and remain static from year-to-year. The need for regional dynamic PET values is vital for improved operational forecasting. With the advent of satellite remote sensing and the adoption of a more flexible operational forecast system by the National Weather Service, incorporation of advanced data products is now more feasible than in years past. In this study, we will test a previously developed satellite-derived PET product (UCLA MODIS-PET) in the National Weather Service forecast models and compare the model results to current methods. The UCLA MODIS-PET method is based on the Priestley-Taylor formulation, is driven with MODIS satellite products, and produces a daily, 250m PET estimate. The focus area is eight headwater basins in the upper Midwest U.S. There is a need to develop improved forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This region is highly flood prone yet sensitive to prolonged dry periods in late summer and early fall, and is characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. Our goal is to improve model simulations, and thereby, the initial conditions prior to the start of a forecast through the use of PET values that better reflect actual watershed conditions. The forecast models are being tested in both distributed and lumped mode.

Forecasting the Revenues of Local Public Health Departments in the Shadows of the ‘Great Recession’

PubMed Central

Reschovsky, Andrew; Zahner, Susan J.

2015-01-01

Context The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the “Great Recession” on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy-makers. This study represents a novel research application for these econometric methods. Methods Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an eight year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in “base case” estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. Findings The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014 inflation-adjusted revenues will decline by $8 million, a reduction of 4.7 percent. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6 percent between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. Conclusions The impact of the Great Recession continues to haunt LHDs. Multi-year revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided. PMID:23531611

Forecasting the Revenues of Local Public Health Departments in the Shadows of the "Great Recession".

PubMed

Reschovsky, Andrew; Zahner, Susan J

2016-01-01

The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the "Great Recession" on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy makers. This study represents a novel research application for these econometric methods. Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an 8-year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in "base case" estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014, inflation-adjusted revenues will decline by $8 million, a reduction of 4.7%. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6% between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. The impact of the Great Recession continues to haunt LHDs. Multiyear revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided.

Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus

PubMed Central

Smith, Jack W.; Everhart, J.E.; Dickson, W.C.; Knowler, W.C.; Johannes, R.S.

1988-01-01

Neural networks or connectionist models for parallel processing are not new. However, a resurgence of interest in the past half decade has occurred. In part, this is related to a better understanding of what are now referred to as hidden nodes. These algorithms are considered to be of marked value in pattern recognition problems. Because of that, we tested the ability of an early neural network model, ADAP, to forecast the onset of diabetes mellitus in a high risk population of Pima Indians. The algorithm's performance was analyzed using standard measures for clinical tests: sensitivity, specificity, and a receiver operating characteristic curve. The crossover point for sensitivity and specificity is 0.76. We are currently further examining these methods by comparing the ADAP results with those obtained from logistic regression and linear perceptron models using precisely the same training and forecasting sets. A description of the algorithm is included.

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

PubMed

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

State-space modeling to support management of brucellosis in the Yellowstone bison population

USGS Publications Warehouse

Hobbs, N. Thompson; Geremia, Chris; Treanor, John; Wallen, Rick; White, P.J.; Hooten, Mevin B.; Rhyan, Jack C.

2015-01-01

The bison (Bison bison) of the Yellowstone ecosystem, USA, exemplify the difficulty of conserving large mammals that migrate across the boundaries of conservation areas. Bison are infected with brucellosis (Brucella abortus) and their seasonal movements can expose livestock to infection. Yellowstone National Park has embarked on a program of adaptive management of bison, which requires a model that assimilates data to support management decisions. We constructed a Bayesian state-space model to reveal the influence of brucellosis on the Yellowstone bison population. A frequency-dependent model of brucellosis transmission was superior to a density-dependent model in predicting out-of-sample observations of horizontal transmission probability. A mixture model including both transmission mechanisms converged on frequency dependence. Conditional on the frequency-dependent model, brucellosis median transmission rate was 1.87 yr−1. The median of the posterior distribution of the basic reproductive ratio (R0) was 1.75. Seroprevalence of adult females varied around 60% over two decades, but only 9.6 of 100 adult females were infectious. Brucellosis depressed recruitment; estimated population growth rate λ averaged 1.07 for an infected population and 1.11 for a healthy population. We used five-year forecasting to evaluate the ability of different actions to meet management goals relative to no action. Annually removing 200 seropositive female bison increased by 30-fold the probability of reducing seroprevalence below 40% and increased by a factor of 120 the probability of achieving a 50% reduction in transmission probability relative to no action. Annually vaccinating 200 seronegative animals increased the likelihood of a 50% reduction in transmission probability by fivefold over no action. However, including uncertainty in the ability to implement management by representing stochastic variation in the number of accessible bison dramatically reduced the probability of achieving goals using interventions relative to no action. Because the width of the posterior predictive distributions of future population states expands rapidly with increases in the forecast horizon, managers must accept high levels of uncertainty. These findings emphasize the necessity of iterative, adaptive management with relatively short-term commitment to action and frequent reevaluation in response to new data and model forecasts. We believe our approach has broad applications.

Forecasting High-Priority Infectious Disease Surveillance Regions: A Socioeconomic Model

PubMed Central

Chan, Emily H.; Scales, David A.; Brewer, Timothy F.; Madoff, Lawrence C.; Pollack, Marjorie P.; Hoen, Anne G.; Choden, Tenzin; Brownstein, John S.

2013-01-01

Background. Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. Methods. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Results. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996–2008. Conclusions. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions. PMID:23118271

Disaggregating residential water demand for improved forecasts and decision making

NASA Astrophysics Data System (ADS)

Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

2003-04-01

Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water-using fixtures, and ranges from hedonism (installing pools and whirlpool tubs) to satisficing (adjusting irrigation timers only twice per year) to acting on deeply-held conservation ethics in ways that not only fail any benefit-cost test, but are discouraged, or even illegal (reuse of gray water and black water). Research findings are being captured in dynamic simulation models that integrate social and natural science to create tools to assist water resource managers in providing sustainable water supplies and improving residential water demand forecasts. These models feature simple, graphical user interfaces and output screens that provide decision makers with visual, easy-to-understand information at the basin level. The models reveal connections between various supply and demand components, and highlight direct impacts and feedback mechanisms associated with various policy options.

PACIFIC NORTHWEST SALMON: FORECASTING THEIR STATUS IN 2100

EPA Science Inventory

Throughout the Pacific Northwest (northern California, Oregon, Idaho, Washington, and the Columbia Basin portion of British Columbia), many wild salmon stocks (a group of interbreeding individuals that is roughly equivalent to a "population") have declined and some have disappear...

United States geological survey's reserve-growth models and their implementation

USGS Publications Warehouse

Klett, T.R.

2005-01-01

The USGS has developed several mathematical models to forecast reserve growth of fields both in the United States (U.S.) and the world. The models are based on historical reserve growth patterns of fields in the U.S. The patterns of past reserve growth are extrapolated to forecast future reserve growth. Changes of individual field sizes through time are extremely variable, therefore, the reserve growth models take on a statistical approach whereby volumetric changes for populations of fields are used in the models. Field age serves as a measure of the field-development effort that is applied to promote reserve growth. At the time of the USGS World Petroleum Assessment 2000, a reserve growth model for discovered fields of the world was not available. Reserve growth forecasts, therefore, were made based on a model of historical reserve growth of fields of the U.S. To test the feasibility of such an application, reserve growth forecasts were made of 186 giant oil fields of the world (excluding the U.S. and Canada). In addition, forecasts were made for these giant oil fields subdivided into those located in and outside of Organization of Petroleum Exporting Countries (OPEC). The model provided a reserve-growth forecast that closely matched the actual reserve growth that occurred from 1981 through 1996 for the 186 fields as a whole, as well as for both OPEC and non-OPEC subdivisions, despite the differences in reserves definition among the fields of the U.S. and the rest of the world. ?? 2005 International Association for Mathematical Geology.

Definition of perspective scheme of organization of traffic using methods of forecasting and modeling

NASA Astrophysics Data System (ADS)

Vlasov, V. M.; Novikov, A. N.; Novikov, I. A.; Shevtsova, A. G.

2018-03-01

In the environment of highly developed urban agglomerations, one of the main problems arises - inability of the road network to reach a high level of motorization. The introduction of intelligent transport systems allows solving this problem, but the main issue in their implementation remains open: to what extent this or that method of improving the transport network will be effective and whether it is able to solve the problem of vehicle growth especially for the long-term period. The main goal of this work was the development of an approach to forecasting the increase in the intensity of traffic flow for a long-term period using the population and the level of motorization. The developed approach made it possible to determine the projected population and, taking into account the level of motorization, to determine the growth factor of the traffic flow intensity, which allows calculating the intensity value for a long-term period with high accuracy. The analysis of the main methods for predicting the characteristics of the transport stream is performed. The basic values and parameters necessary for their use are established. The analysis of the urban settlement is carried out and the level of motorization characteristic for the given locality is determined. A new approach to predicting the intensity of the traffic flow has been developed, which makes it possible to predict the change in the transport situation in the long term in high accuracy. Calculations of the magnitude of the intensity increase on the basis of the developed forecasting method are made and the errors in the data obtained are determined. The main recommendations on the use of the developed forecasting approach for the long-term functioning of the road network are formulated.

Coupling Recruitment Forecasts with Economics in the Gulf of Maine's American Lobster Fishery

NASA Astrophysics Data System (ADS)

Wahle, R.; Oppenheim, N.; Brady, D. C.; Dayton, A.; Sun, C. H. J.

2016-02-01

Accurate predictions of fishery recruitment and landings represent an important goal of fisheries science and management, but linking environmental drivers of fish population dynamics to financial markets remains a challenge. A fundamental step in that process is understanding the environmental drivers of fishery recruitment. American lobster (Homarus americanus) populations of the northwest Atlantic have been undergoing a dramatic surge, mostly driven by increases the Gulf of Maine. Settler-recruit models that track cohorts after larvae settle to the sea bed are proving useful in predicting subsequent fishery recruitment some 5-7 years later. Here we describe new recruitment forecasting models for the lobster fishery at 11 management areas from Southern New England to Atlantic Canada. We use an annual survey of juvenile year-class strength and environmental indicators to parameterize growth and mortality terms in the model. As a consequence of a recent widespread multi-year downturn in larval settlement, our models suggest that the peak in lobster abundance in the Gulf of Maine will be passed in the near future. We also present initial steps in the coupling of forecast data with economic models for the fishery. We anticipate that these models will give stakeholders and policy makers time to consider their management choices for this most valuable of the region's fisheries. Our vision is to couple our forecast model outputs to an economic model that captures the dynamics of market forces in the New England and Canadian Maritime lobster fisheries. It will then be possible to estimate the financial status of the fishery several years in advance. This early warning system could mitigate the adverse effects of a fluctuating fishery on the coastal communities that are perilously dependent upon it.

Process-based modeling of species' responses to climate change - a proof of concept using western North American trees

NASA Astrophysics Data System (ADS)

Evans, M. E.; Merow, C.; Record, S.; Menlove, J.; Gray, A.; Cundiff, J.; McMahon, S.; Enquist, B. J.

2013-12-01

Current attempts to forecast how species' distributions will change in response to climate change suffer under a fundamental trade-off: between modeling many species superficially vs. few species in detail (between correlative vs. mechanistic models). The goals of this talk are two-fold: first, we present a Bayesian multilevel modeling framework, dynamic range modeling (DRM), for building process-based forecasts of many species' distributions at a time, designed to address the trade-off between detail and number of distribution forecasts. In contrast to 'species distribution modeling' or 'niche modeling', which uses only species' occurrence data and environmental data, DRMs draw upon demographic data, abundance data, trait data, occurrence data, and GIS layers of climate in a single framework to account for two processes known to influence range dynamics - demography and dispersal. The vision is to use extensive databases on plant demography, distributions, and traits - in the Botanical Information and Ecology Network, the Forest Inventory and Analysis database (FIA), and the International Tree Ring Data Bank - to develop DRMs for North American trees. Second, we present preliminary results from building the core submodel of a DRM - an integral projection model (IPM) - for a sample of dominant tree species in western North America. IPMs are used to infer demographic niches - i.e., the set of environmental conditions under which population growth rate is positive - and project population dynamics through time. Based on >550,000 data points derived from FIA for nine tree species in western North America, we show IPM-based models of their current and future distributions, and discuss how IPMs can be used to forecast future forest productivity, mortality patterns, and inform efforts at assisted migration.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season.

PubMed

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-09-01

Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States.

PubMed

Watson, Stella C; Liu, Yan; Lund, Robert B; Gettings, Jenna R; Nordone, Shila K; McMahan, Christopher S; Yabsley, Michael J

2017-01-01

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States

PubMed Central

Watson, Stella C.; Liu, Yan; Lund, Robert B.; Gettings, Jenna R.; Nordone, Shila K.; McMahan, Christopher S.

2017-01-01

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge. PMID:28472096

USING ECO-EVOLUTIONARY INDIVIDUAL-BASED MODELS TO INVESTIGATE SPATIALLY-DEPENDENT PROCESSES IN CONSERVATION GENETICS

EPA Science Inventory

Eco-evolutionary population simulation models are powerful new forecasting tools for exploring management strategies for climate change and other dynamic disturbance regimes. Additionally, eco-evo individual-based models (IBMs) are useful for investigating theoretical feedbacks ...

Rate of recovery from perturbations as a means to forecast future stability of living systems.

PubMed

Ghadami, Amin; Gourgou, Eleni; Epureanu, Bogdan I

2018-06-18

Anticipating critical transitions in complex ecological and living systems is an important need because it is often difficult to restore a system to its pre-transition state once the transition occurs. Recent studies demonstrate that several indicators based on changes in ecological time series can indicate that the system is approaching an impending transition. An exciting question is, however, whether we can predict more characteristics of the future system stability using measurements taken away from the transition. We address this question by introducing a model-less forecasting method to forecast catastrophic transition of an experimental ecological system. The experiment is based on the dynamics of a yeast population, which is known to exhibit a catastrophic transition as the environment deteriorates. By measuring the system's response to perturbations prior to transition, we forecast the distance to the upcoming transition, the type of the transition (i.e., catastrophic/non-catastrophic) and the future equilibrium points within a range near the transition. Experimental results suggest a strong potential for practical applicability of this approach for ecological systems which are at risk of catastrophic transitions, where there is a pressing need for information about upcoming thresholds.

Bayesian Probabilistic Projections of Life Expectancy for All Countries

PubMed Central

Raftery, Adrian E.; Chunn, Jennifer L.; Gerland, Patrick; Ševčíková, Hana

2014-01-01

We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world from the present to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we did an out-of-sample cross-validation experiment, fitting the model to the data from 1950–1995, and using the estimated model to forecast for the subsequent ten years. The ten-year predictions had a mean absolute error of about 1 year, about 40% less than the current UN methodology. The probabilistic forecasts were calibrated, in the sense that (for example) the 80% prediction intervals contained the truth about 80% of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free publicly available R software packages called bayesLife and bayesDem are available to implement the method. PMID:23494599

Oviposition traps to survey eggs of Lambdina fiscellaria (Lepidoptera: Geometridae).

PubMed

Hébert, Christian; Jobin, Luc; Auger, Michel; Dupont, Alain

2003-06-01

Outbreaks of the hemlock looper, Lambdina fiscellaria (Gueneé), are characterized by rapid increase and patchy distribution over widespread areas, which make it difficult to detect impending outbreaks. This is a major problem with this insect. Population forecasting is based on tedious and expensive egg surveys in which eggs are extracted from 1-m branches; careful observation is needed to avoid counting old unhatched eggs of previous year populations. The efficacy of artificial substrates as oviposition traps to sample hemlock looper eggs was tested as a means of improving outbreak detection and population forecasting. A white polyurethane foam substrate (1,095 lb/ft3) used with the Luminoc insect trap, a portable light trap, was highly efficient in sampling eggs of the hemlock looper. Foam strips placed on tree trunks at breast height were less efficient but easier and less expensive to use for the establishment of extensive survey networks. Estimates based on oviposition traps were highly correlated with those obtained from the 1-m branch extraction method. The oviposition trap is a standard, inexpensive, easy, and robust method that can be used by nonspecialists. This technique makes it possible to sample higher numbers of plots in widespread monitoring networks, which is crucial for improving the management of hemlock looper populations.

Calls Forecast for the Moscow Ambulance Service. The Impact of Weather Forecast

NASA Astrophysics Data System (ADS)

Gordin, Vladimir; Bykov, Philipp

2015-04-01

We use the known statistics of the calls for the current and previous days to predict them for tomorrow and for the following days. We assume that this algorithm will work operatively, will cyclically update the available information and will move the horizon of the forecast. Sure, the accuracy of such forecasts depends on their lead time, and from a choice of some group of diagnoses. For comparison we used the error of the inertial forecast (tomorrow there will be the same number of calls as today). Our technology has demonstrated accuracy that is approximately two times better compared to the inertial forecast. We obtained the following result: the number of calls depends on the actual weather in the city as well as on its rate of change. We were interested in the accuracy of the forecast for 12-hour sum of the calls in real situations. We evaluate the impact of the meteorological errors [1] on the forecast errors of the number of Ambulance calls. The weather and the Ambulance calls number both have seasonal tendencies. Therefore, if we have medical information from one city only, we should separate the impacts of such predictors as "annual variations in the number of calls" and "weather". We need to consider the seasonal tendencies (associated, e. g. with the seasonal migration of the population) and the impact of the air temperature simultaneously, rather than sequentially. We forecasted separately the number of calls with diagnoses of cardiovascular group, where it was demonstrated the advantage of the forecasting method, when we use the maximum daily air temperature as a predictor. We have a chance to evaluate statistically the influence of meteorological factors on the dynamics of medical problems. In some cases it may be useful for understanding of the physiology of disease and possible treatment options. We can assimilate some personal archives of medical parameters for the individuals with concrete diseases and the relative meteorological archive. As a result we hope to evaluate how weather can influence the intensity of the disease. Thus, the knowledge of the weather forecast for several days will help us to predict a state of health. The person will be able to take some proactive actions to avoid the anticipated worsening of his health. Literature 1. A. N. Bagrov, F. L. Bykov, V. A. Gordin. Complex Forecast of Surface Meteorological Parameters. Meteorology and Hydrology, 2014, N 5, 5-16 (Russian), 283-291 (English). 2. Bykov, Ph.L., Gordin, V.A., Objective Analysis of the Structure of Three-Dimensional Atmospheric Fronts. Izvestia of Russian Academy of Sciences. Ser. The Physics of Atmosphere and Ocean, 48 (2) (2012), 172-188 (Russian), 152-168 (English), http://dx.doi.org/10.1134/S0001433812020053 3. V.A.Gordin. Mathematical Problems and Methods in Hydrodynamical Weather Forecasting. Amsterdam etc.: Gordon & Breach Publ. House, 2000. 4. V.A.Gordin. Mathematics, Computer, Weather Forecasting, and Other Mathematical Physics' Scenarios. Moscow, Fizmatlit, 2010, 2012 (Russian).

Synthesis of traveler choice research: improving modeling accuracy for better transportation decisionmaking.

DOT National Transportation Integrated Search

2013-08-01

"Over the last 50 years, advances in the fields of travel behavior research and travel demand forecasting have been : immense, driven by the increasing costs of infrastructure and spatial limitations in areas of high population density : together wit...

7 CFR 3700.3 - Functions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... and related farm and trade developments and short to long-term forecasts of domestic and world... world agricultural markets. (3) Conducting special analyses of U.S. and world agricultural markets for... trends in the non-metropolitan and farm populations, the number, location and characteristics of such...

Development of a flood early warning system and communication with end-users: the Vipava/Vipacco case study in the KULTURisk FP7 project

NASA Astrophysics Data System (ADS)

Grossi, Giovanna; Caronna, Paolo; Ranzi, Roberto

2014-05-01

Within the framework of risk communication, the goal of an early warning system is to support the interaction between technicians and authorities (and subsequently population) as a prevention measure. The methodology proposed in the KULTURisk FP7 project aimed to build a closer collaboration between these actors, in the perspective of promoting pro-active actions to mitigate the effects of flood hazards. The transnational (Slovenia/ Italy) Soča/Isonzo case study focused on this concept of cooperation between stakeholders and hydrological forecasters. The DIMOSHONG_VIP hydrological model was calibrated for the Vipava/Vipacco River (650 km2), a tributary of the Soča/Isonzo River, on the basis of flood events occurred between 1998 and 2012. The European Centre for Medium-Range Weather Forecasts (ECMWF) provided the past meteorological forecasts, both deterministic (1 forecast) and probabilistic (51 ensemble members). The resolution of the ECMWF grid is currently about 15 km (Deterministic-DET) and 30 km (Ensemble Prediction System-EPS). A verification was conducted to validate the flood-forecast outputs of the DIMOSHONG_VIP+ECMWF early warning system. Basic descriptive statistics, like event probability, probability of a forecast occurrence and frequency bias were determined. Some performance measures were calculated, such as hit rate (probability of detection) and false alarm rate (probability of false detection). Relative Opening Characteristic (ROC) curves were generated both for deterministic and probabilistic forecasts. These analysis showed a good performance of the early warning system, in respect of the small size of the sample. A particular attention was spent to the design of flood-forecasting output charts, involving and inquiring stakeholders (Alto Adriatico River Basin Authority), hydrology specialists in the field, and common people. Graph types for both forecasted precipitation and discharge were set. Three different risk thresholds were identified ("attention", "pre-alarm" or "alert", "alarm"), with an "icon-style" representation, suitable for communication to civil protection stakeholders or the public. Aiming at showing probabilistic representations in a "user-friendly" way, we opted for the visualization of the single deterministic forecasted hydrograph together with the 5%, 25%, 50%, 75% and 95% percentiles bands of the Hydrological Ensemble Prediction System (HEPS). HEPS is generally used for 3-5 days hydrological forecasts, while the error due to incorrect initial data is comparable to the error due to the lower resolution with respect to the deterministic forecast. In the short term forecasting (12-48 hours) the HEPS-members show obviously a similar tendency; in this case, considering its higher resolution, the deterministic forecast is expected to be more effective. The plot of different forecasts in the same chart allows the use of model outputs from 4/5 days to few hours before a potential flood event. This framework was built to help a stakeholder, like a mayor, a civil protection authority, etc, in the flood control and management operations, and was designed to be included in a wider decision support system.

Forecasting need and demand for home health care: a selective review

PubMed Central

Sharma, Rabinder K.

1980-01-01

Three models for forecasting home health care (HHC) needs are analyzed: HSA/SP model (Health Systems Agency of Southwestern Pennsylvania); Florida model (Florida State Department of Health and Rehabilitative Services); and Rhode Island model (Rhode Island Department of Community Affairs). A utilization approach to forecasting is also presented. In the HSA/SP and Florida models, need for HHC is based on a certain proportion of (a) hospital admissions and (b) patients entering HHC from other sources. The major advantage of these models is that they are relatively easy to use and explain; their major weaknesses are an imprecise definition of need and an incomplete model specification. The Rhode Island approach defines need for HHC in terms of the health status of the population as measured by chronic activity limitations. The major strengths of this approach are its explicit assumptions and its emphasis on consumer needs. The major drawback is that it requires considerable local area data. The utilization approach is based on extrapolation from observed utilization experience of the target population. Its main limitation is that it is based on current market imperfections; its major advantage is that it exposes existing deficiencies in HHC. The author concludes that each approach should be tested empirically in order to refine it, and that need and demand approaches be used jointly in the planning process. PMID:6893631

Historical view and future demand for knee arthroplasty in Sweden

PubMed Central

Rolfson, Ola; W-Dahl, Annette; Garellick, Göran; Sundberg, Martin; Kärrholm, Johan; Robertsson, Otto

2015-01-01

Background and purpose The incidence of knee osteoarthritis will most likely increase. We analyzed historical trends in the incidence of knee arthroplasty in Sweden between 1975 and 2013, in order to be able to provide projections of future demand. Patients and methods We obtained information on all knee arthroplasties in Sweden in the period 1975–2013 from the Swedish Knee Arthroplasty Register, and used public domain data from Statistics Sweden on the evolution of and forecasts for the Swedish population. We forecast the incidence, presuming the existence of a maximum incidence. Results We found that the incidence of knee arthroplasty will continue to increase until a projected upper incidence level of about 469 total knee replacements per 105 Swedish residents aged 40 years and older is reached around the year 2130. In 2020, the estimated incidence of total knee arthroplasties per 105 Swedish residents aged 40 years and older will be 334 (95% prediction interval (PI): 281–374) and in 2030 it will be 382 (PI: 308–441). Using officially forecast population growth data, around 17,500 operations would be expected to be performed in 2020 and around 21,700 would be expected to be performed in 2030. Interpretation Today’s levels of knee arthroplasty are well below the expected maximum incidence, and we expect a continued annual increase in the total number of knee arthroplasties performed. PMID:25806653

Using multiple data sets to populate probabilistic volcanic event trees

USGS Publications Warehouse

Newhall, C.G.; Pallister, John S.

2014-01-01

The key parameters one needs to forecast outcomes of volcanic unrest are hidden kilometers beneath the Earth’s surface, and volcanic systems are so complex that there will invariably be stochastic elements in the evolution of any unrest. Fortunately, there is sufficient regularity in behaviour that some, perhaps many, eruptions can be forecast with enough certainty for populations to be evacuated and kept safe. Volcanologists charged with forecasting eruptions must try to understand each volcanic system well enough that unrest can be interpreted in terms of pre-eruptive process, but must simultaneously recognize and convey uncertainties in their assessment. We have found that use of event trees helps to focus discussion, integrate data from multiple sources, reach consensus among scientists about both pre-eruptive process and uncertainties and, in some cases, to explain all of this to officials. Figure 1 shows a generic volcanic event tree from Newhall and Hoblitt (2002) that can be modified as needed for each specific volcano. This paper reviews how we and our colleagues have used such trees during a number of volcanic crises worldwide, for rapid hazard assessments in situations in which more formal expert elicitations could not be conducted. We describe how Multiple Data Sets can be used to estimate probabilities at each node and branch. We also present case histories of probability estimation during crises, how the estimates were used by public officials, and some suggestions for future improvements.

Historic and forecasted population and land-cover change in eastern North Carolina, 1992-2030

USGS Publications Warehouse

Claggett, Peter; Hearn,, Paul P.; Donato, David I.

2015-01-01

The Southeast Regional Partnership for Planning and Sustainability (SERPPAS) was formed in 2005 as a partnership between the Department of Defense (DOD) and State and Federal agencies to promote better collaboration in making resource-use decisions. In support of this goal, the U.S. Geological Survey (USGS) conducted a study to evaluate historic population growth and land-cover change, and to model future change, for the 13-county SERPPAS study area in southeastern North Carolina (fig. 1). Improved understanding of trends in land-cover change and the ability to forecast land-cover change that is consistent with these trends will be a key component of efforts to accommodate local military-mission imperatives while also promoting sustainable economic growth throughout the 13-county study area. The study had three principal objectives:    1.  Evaluate historic changes in population and land cover for the period 1992–2006 using both previously existing as well as newly generated land-cover data.    2.  Develop models to forecast future change in land cover using the data gathered in objective 1 in conjunction with ancillary data on the suitability of the various sub-areas within the study area for low- and high-intensity urban development.    3.  Deliver these results—including an executive-level briefing and a USGS technical report—to DOD, other project cooperators, and local counties in hard-copy and digital formats and via the Web through a map-based data viewer. This report provides a general overview of the study and is intended for general distribution to non-technical audiences.

How wild is your model fire? Constraining WRF-Chem wildfire smoke simulations with satellite observations

NASA Astrophysics Data System (ADS)

Fischer, E. V.; Ford, B.; Lassman, W.; Pierce, J. R.; Pfister, G.; Volckens, J.; Magzamen, S.; Gan, R.

2015-12-01

Exposure to high concentrations of particulate matter (PM) present during acute pollution events is associated with adverse health effects. While many anthropogenic pollution sources are regulated in the United States, emissions from wildfires are difficult to characterize and control. With wildfire frequency and intensity in the western U.S. projected to increase, it is important to more precisely determine the effect that wildfire emissions have on human health, and whether improved forecasts of these air pollution events can mitigate the health risks associated with wildfires. One of the challenges associated with determining health risks associated with wildfire emissions is that the low spatial resolution of surface monitors means that surface measurements may not be representative of a population's exposure, due to steep concentration gradients. To obtain better estimates of ambient exposure levels for health studies, a chemical transport model (CTM) can be used to simulate the evolution of a wildfire plume as it travels over populated regions downwind. Improving the performance of a CTM would allow the development of a new forecasting framework that could better help decision makers estimate and potentially mitigate future health impacts. We use the Weather Research and Forecasting model with online chemistry (WRF-Chem) to simulate wildfire plume evolution. By varying the model resolution, meteorology reanalysis initial conditions, and biomass burning inventories, we are able to explore the sensitivity of model simulations to these various parameters. Satellite observations are used first to evaluate model skill, and then to constrain the model results. These data are then used to estimate population-level exposure, with the aim of better characterizing the effects that wildfire emissions have on human health.

Population projections for AIDS using an actuarial model.

PubMed

Wilkie, A D

1989-09-05

This paper gives details of a model for forecasting AIDS, developed for actuarial purposes, but used also for population projections. The model is only appropriate for homosexual transmission, but it is age-specific, and it allows variation in the transition intensities by age, duration in certain states and calendar year. The differential equations controlling transitions between states are defined, the method of numerical solution is outlined, and the parameters used in five different Bases of projection are given in detail. Numerical results for the population of England and Wales are shown.

Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

DOE PAGES

Pelletier, Jon D.; Murray, A. Brad; Pierce, Jennifer L.; ...

2015-07-14

In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we havemore » the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.« less

Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services

PubMed Central

Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R.; Rodó, Xavier

2016-01-01

Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better short-to-medium-term resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g., 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions, inferred using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for S2S climate forecasts to be incorporated in localised heat–health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe. PMID:26861369

Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

NASA Astrophysics Data System (ADS)

Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

2010-05-01

The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services.

PubMed

Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R; Rodó, Xavier

2016-02-06

Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better short-to-medium-term resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g., 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions, inferred using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for S2S climate forecasts to be incorporated in localised heat-health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

PubMed Central

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D.; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-01-01

ABSTRACT Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Implications: Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders. PMID:26934496

The impact of underwater glider observations in the forecast of Hurricane Gonzalo (2014)

NASA Astrophysics Data System (ADS)

Goni, G. J.; Domingues, R. M.; Kim, H. S.; Domingues, R. M.; Halliwell, G. R., Jr.; Bringas, F.; Morell, J. M.; Pomales, L.; Baltes, R.

2017-12-01

The tropical Atlantic basin is one of seven global regions where tropical cyclones (TC) are commonly observed to originate and intensify from June to November. On average, approximately 12 TCs travel through the region every year, frequently affecting coastal, and highly populated areas. In an average year, 2 to 3 of them are categorized as intense hurricanes. Given the appropriate atmospheric conditions, TC intensification has been linked to ocean conditions, such as increased ocean heat content and enhanced salinity stratification near the surface. While errors in hurricane track forecasts have been reduced during the last years, errors in intensity forecasts remain mostly unchanged. Several studies have indicated that the use of in situ observations has the potential to improve the representation of the ocean to correctly initialize coupled hurricane intensity forecast models. However, a sustained in situ ocean observing system in the tropical North Atlantic Ocean and Caribbean Sea dedicated to measuring subsurface thermal and salinity fields in support of TC intensity studies and forecasts has yet to be implemented. Autonomous technologies offer new and cost-effective opportunities to accomplish this objective. We highlight here a partnership effort that utilize underwater gliders to better understand air-sea processes during high wind events, and are particularly geared towards improving hurricane intensity forecasts. Results are presented for Hurricane Gonzalo (2014), where glider observations obtained in the tropical Atlantic: Helped to provide an accurate description of the upper ocean conditions, that included the presence of a low salinity barrier layer; Allowed a detailed analysis of the upper ocean response to hurricane force winds of Gonzalo; Improved the initialization of the ocean in a coupled ocean-atmosphere numerical model; and together with observations from other ocean observing platforms, substantially reduced the error in intensity forecast using the HYCOM-HWRF model. Data collected by this project are transmitted in real-time to the Global Telecommunication System, distributed through the institutional web pages, by the IOOS Glider Data Assembly Center, and by NCEI, and assimilated in real-time numerical weather forecast models.

Recruiting Implications of the Long War for the Marine Corps

DTIC Science & Technology

2008-01-01

forecast future demographic complexion. Thus today’s marketing and advertising efforts can be tailored to shape tomorrow’s desired force diversity... marketing and advertising campaign. Continue to place Hispanic recruiters in urban centers with dense Hispanic population. Lastly, the Marine Corps

Forecasting Bromus tectorum and fire threat: site soil type versus population traits

USDA-ARS?s Scientific Manuscript database

Cheatgrass (Bromus tectorum), is an exotic invasive annual grass that increases the chance, rate, spread and season of wildfires. Cheatgrass truncates secondary succession by out-competing native perennial seedlings for limited moisture and resources. Habitats that historically burned every 60-110...

Feasibility of Forecasting Highway Safety in Support of Safety Incentive and Safety Target Programs.

DOT National Transportation Integrated Search

2007-11-01

Using the frequency of fatal crashes from the current observation period (e.g. month, year, etc.) as the : prediction of expected future performance does not account for changes in safety that result from : increases in exposure (population, addition...

Towards an operational high-resolution air quality forecasting system at ECCC

NASA Astrophysics Data System (ADS)

Munoz-Alpizar, Rodrigo; Stroud, Craig; Ren, Shuzhan; Belair, Stephane; Leroyer, Sylvie; Souvanlasy, Vanh; Spacek, Lubos; Pavlovic, Radenko; Davignon, Didier; Moran, Moran

2017-04-01

Urban environments are particularly sensitive to weather, air quality (AQ), and climatic conditions. Despite the efforts made in Canada to reduce pollution in urban areas, AQ continues to be a concern for the population, especially during short-term episodes that could lead to exceedances of daily air quality standards. Furthermore, urban air pollution has long been associated with significant adverse health effects. In Canada, the large percentage of the population living in urban areas ( 81%, according to the Canada's 2011 census) is exposed to elevated air pollution due to local emissions sources. Thus, in order to improve the services offered to the Canadian public, Environment and Climate Change Canada has launched an initiative to develop a high-resolution air quality prediction capacity for urban areas in Canada. This presentation will show observed pollution trends (2010-2016) for Canadian mega-cities along with some preliminary high-resolution air quality modelling results. Short-term and long-term plans for urban AQ forecasting in Canada will also be described.

Understanding causality and uncertainty in volcanic observations: An example of forecasting eruptive activity on Soufrière Hills Volcano, Montserrat

NASA Astrophysics Data System (ADS)

Sheldrake, T. E.; Aspinall, W. P.; Odbert, H. M.; Wadge, G.; Sparks, R. S. J.

2017-07-01

Following a cessation in eruptive activity it is important to understand how a volcano will behave in the future and when it may next erupt. Such an assessment can be based on the volcano's long-term pattern of behaviour and insights into its current state via monitoring observations. We present a Bayesian network that integrates these two strands of evidence to forecast future eruptive scenarios using expert elicitation. The Bayesian approach provides a framework to quantify the magmatic causes in terms of volcanic effects (i.e., eruption and unrest). In October 2013, an expert elicitation was performed to populate a Bayesian network designed to help forecast future eruptive (in-)activity at Soufrière Hills Volcano. The Bayesian network was devised to assess the state of the shallow magmatic system, as a means to forecast the future eruptive activity in the context of the long-term behaviour at similar dome-building volcanoes. The findings highlight coherence amongst experts when interpreting the current behaviour of the volcano, but reveal considerable ambiguity when relating this to longer patterns of volcanism at dome-building volcanoes, as a class. By asking questions in terms of magmatic causes, the Bayesian approach highlights the importance of using short-term unrest indicators from monitoring data as evidence in long-term forecasts at volcanoes. Furthermore, it highlights potential biases in the judgements of volcanologists and identifies sources of uncertainty in terms of magmatic causes rather than scenario-based outcomes.

Science implementation of Forecast Mekong for food and environmental security

USGS Publications Warehouse

Turnipseed, D. Phil

2012-01-01

Forecast Mekong is a significant international thrust under the Delta Research and Global Observation Network (DRAGON) of the U.S. Geological Survey (USGS) and was launched in 2009 by the U.S. Department of State and the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam under U.S. Department of State Secretary Hillary R. Clinton's Lower Mekong Initiative to enhance U.S. engagement with countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. Since 2009, the USGS has worked closely with the U.S. Department of State; personnel from Cambodia, Laos, Thailand, and Vietnam; nongovernmental organizations; and academia to collect and use research and data from the Lower Mekong River Basin to provide hands-on results that will help decisionmakers in future planning and design for restoration, conservation, and management efforts in the Lower Mekong River Basin. In 2012 Forecast Mekong is highlighting the increasing cooperation between the United States and Lower Mekong River Basin countries in the areas of food and environmental security. Under the DRAGON, Forecast Mekong continues work in interactive data integration, modeling, and visualization system by initiating three-dimensional bathymetry and river flow data along with a pilot study of fish distribution, population, and migratory patterns in the Lower Mekong River Basin. When fully developed by the USGS, in partnership with local governments and universities throughout the Mekong River region, Forecast Mekong will provide valuable planning tools to visualize the consequences of climate change and river management.

Development of an Impact-Oriented Quantitative Coastal Inundation forecasting and early warning system with social and economic assessment

NASA Astrophysics Data System (ADS)

Fakhruddin, S. H. M.; Babel, Mukand S.; Kawasaki, Akiyuki

2014-05-01

Coastal inundations are an increasing threat to the lives and livelihoods of people living in low-lying, highly-populated coastal areas. According to a World Bank Report in 2005, at least 2.6 million people may have drowned due to coastal inundation, particularly caused by storm surges, over the last 200 years. Forecasting and prediction of natural events, such as tropical and extra-tropical cyclones, inland flooding, and severe winter weather, provide critical guidance to emergency managers and decision-makers from the local to the national level, with the goal of minimizing both human and economic losses. This guidance is used to facilitate evacuation route planning, post-disaster response and resource deployment, and critical infrastructure protection and securing, and it must be available within a time window in which decision makers can take appropriate action. Recognizing this extreme vulnerability of coastal areas to inundation/flooding, and with a view to improve safety-related services for the community, research should strongly enhance today's forecasting, prediction and early warning capabilities in order to improve the assessment of coastal vulnerability and risks and develop adequate prevention, mitigation and preparedness measures. This paper tries to develop an impact-oriented quantitative coastal inundation forecasting and early warning system with social and economic assessment to address the challenges faced by coastal communities to enhance their safety and to support sustainable development, through the improvement of coastal inundation forecasting and warning systems.

Application of the LEPS technique for Quantitative Precipitation Forecasting (QPF) in Southern Italy: a preliminary study

NASA Astrophysics Data System (ADS)

Federico, S.; Avolio, E.; Bellecci, C.; Colacino, M.; Walko, R. L.

2006-03-01

This paper reports preliminary results for a Limited area model Ensemble Prediction System (LEPS), based on RAMS (Regional Atmospheric Modelling System), for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection. To accomplish this task and to limit computational time in an operational implementation of LEPS, we perform a cluster analysis of ECMWF-EPS runs. Starting from the 51 members that form the ECMWF-EPS we generate five clusters. For each cluster a representative member is selected and used to provide initial and dynamic boundary conditions to RAMS, whose integrations generate LEPS. RAMS runs have 12-km horizontal resolution. To analyze the impact of enhanced horizontal resolution on quantitative precipitation forecasts, LEPS forecasts are compared to a full Brute Force (BF) ensemble. This ensemble is based on RAMS, has 36 km horizontal resolution and is generated by 51 members, nested in each ECMWF-EPS member. LEPS and BF results are compared subjectively and by objective scores. Subjective analysis is based on precipitation and probability maps of case studies whereas objective analysis is made by deterministic and probabilistic scores. Scores and maps are calculated by comparing ensemble precipitation forecasts against reports from the Calabria regional raingauge network. Results show that LEPS provided better rainfall predictions than BF for all case studies selected. This strongly suggests the importance of the enhanced horizontal resolution, compared to ensemble population, for Calabria for these cases. To further explore the impact of local physiographic features on QPF (Quantitative Precipitation Forecasting), LEPS results are also compared with a 6-km horizontal resolution deterministic forecast. Due to local and mesoscale forcing, the high resolution forecast (Hi-Res) has better performance compared to the ensemble mean for rainfall thresholds larger than 10mm but it tends to overestimate precipitation for lower amounts. This yields larger false alarms that have a detrimental effect on objective scores for lower thresholds. To exploit the advantages of a probabilistic forecast compared to a deterministic one, the relation between the ECMWF-EPS 700 hPa geopotential height spread and LEPS performance is analyzed. Results are promising even if additional studies are required.

Extreme precipitation forecasting in the Chilean Andean region with complex topography using the Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Gironás, J.; Yáñez Morroni, G.; Caneo, M.; Delgado, R.

2017-12-01

The Weather Research and Forecasting (WRF) model is broadly used for weather forecasting, hindcasting and researching due to its good performance. However, the atmospheric conditions for simulating are not always optimal when it includes complex topographies: affecting WRF mathematical stability and convergence, therefore, its performance. As Chile is a country strongly characterized by a complex topography and high gradients of elevation, WRF is ineffective resolving Chilean mountainous terrain and foothills. The need to own an effective weather forecasting tool relies on that Chile's main cities are located in these regions. Furthermore, the most intense rainfall events take place here, commonly caused by the presence of cutoff lows. This work analyzes a microphysics scheme ensemble to enhance initial forecasts made by the Chilean Weather Agency (DMC). These forecasts were made over the Santiago piedmont, in Quebrada de Ramón watershed, located upstream an urban area highly populated. In this region a non-existing planning increases the potential damage of a flash flood. An initial testing was made over different vertical levels resolution (39 and 50 levels), and subsequently testing with land use and surface models, and finally with the initial and boundary condition data (GFS/FNL). Our task made emphasis in analyzing microphysics and lead time (3 to 5 days before the storm peak) in the computational simulations over three extreme rainfall events between 2015 and 2017. WRF shortcoming are also related to the complex configuration of the synoptic events, even when the steep topography difficult the rainfall event peak amount, and to a lesser degree, the exact rainfall event beginning prediction. No evident trend was found in the lead time, but as expected, better results in rainfall and zero isotherm height are obtained with smaller anticipation. We found that WRF do predict properly the N-hours with the biggest amount of rainfall (5 hours corresponding to Quebrada de Ramón's time of concentration) and the temperatures during the event. This is a fundamental input to a hydrological model that could forecast flash floods. Finally, WSM-6Class microphysics was chosen as the one with best performance, but a geostatistical approach to countervail WRF forecasts' shortcomings over Andean piedmont is required.

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

DOE Office of Scientific and Technical Information (OSTI.GOV)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.

2014-09-12

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressivemore » Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.« less

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

NASA Astrophysics Data System (ADS)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

2014-09-01

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

Application of Catastrophe Risk Modelling to Evacuation Public Policy

NASA Astrophysics Data System (ADS)

Woo, G.

2009-04-01

The decision by civic authorities to evacuate an area threatened by a natural hazard is especially fraught when the population in harm's way is extremely large, and where there is considerable uncertainty in the spatial footprint, scale, and strike time of a hazard event. Traditionally viewed as a hazard forecasting issue, civil authorities turn to scientists for advice on a potentially imminent dangerous event. However, the level of scientific confidence varies enormously from one peril and crisis situation to another. With superior observational data, meteorological and hydrological hazards are generally better forecast than geological hazards. But even with Atlantic hurricanes, the track and intensity of a hurricane can change significantly within a few hours. This complicated and delayed the decision to call an evacuation of New Orleans when threatened by Hurricane Katrina, and would present a severe dilemma if a major hurricane were appearing to head for New York. Evacuation needs to be perceived as a risk issue, requiring the expertise of catastrophe risk modellers as well as geoscientists. Faced with evidence of a great earthquake in the Indian Ocean in December 2004, seismologists were reluctant to give a tsunami warning without more direct sea observations. Yet, from a risk perspective, the risk to coastal populations would have warranted attempts at tsunami warning, even though there was significant uncertainty in the hazard forecast, and chance of a false alarm. A systematic coherent risk-based framework for evacuation decision-making exists, which weighs the advantages of an evacuation call against the disadvantages. Implicitly and qualitatively, such a cost-benefit analysis is undertaken by civic authorities whenever an evacuation is considered. With the progress in catastrophe risk modelling, such an analysis can be made explicit and quantitative, providing a transparent audit trail for the decision process. A stochastic event set, the core of a catastrophe risk model, is required to explore the casualty implications of different possible hazard scenarios, to assess the proportion of an evacuated population who would owe their lives to an evacuation, and to estimate the economic loss associated with an unnecessary evacuation. This paper will review the developing methodology for applying catastrophe risk modelling to support public policy in evacuation decision-making, and provide illustrations from across the range of natural hazards. Evacuation during volcanic crises is a prime example, recognizing the improving forecasting skill of volcanologists, now able to account probabilistically for precursory seismological, geodetic, and geochemical monitoring data. This methodology will be shown to help civic authorities make sounder risk-informed decisions on the timing and population segmentation of evacuation from both volcanoes and calderas, such as Vesuvius and Campi Flegrei, which are in densely populated urban regions.

Using Landslide Failure Forecast Models in Near Real Time: the Mt. de La Saxe case-study

NASA Astrophysics Data System (ADS)

Manconi, Andrea; Giordan, Daniele

2014-05-01

Forecasting the occurrence of landslide phenomena in space and time is a major scientific challenge. The approaches used to forecast landslides mainly depend on the spatial scale analyzed (regional vs. local), the temporal range of forecast (long- vs. short-term), as well as the triggering factor and the landslide typology considered. By focusing on short-term forecast methods for large, deep seated slope instabilities, the potential time of failure (ToF) can be estimated by studying the evolution of the landslide deformation over time (i.e., strain rate) provided that, under constant stress conditions, landslide materials follow creep mechanism before reaching rupture. In the last decades, different procedures have been proposed to estimate ToF by considering simplified empirical and/or graphical methods applied to time series of deformation data. Fukuzono, 1985 proposed a failure forecast method based on the experience performed during large scale laboratory experiments, which were aimed at observing the kinematic evolution of a landslide induced by rain. This approach, known also as the inverse-velocity method, considers the evolution over time of the inverse value of the surface velocity (v) as an indicator of the ToF, by assuming that failure approaches while 1/v tends to zero. Here we present an innovative method to aimed at achieving failure forecast of landslide phenomena by considering near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and then apply straightforward statistical methods to obtain confidence intervals on the time of failure. Our results can be relevant to support the management of early warning systems during landslide emergency conditions, also when the predefined displacement and/or velocity thresholds are exceeded. In addition, our statistical approach for the definition of confidence interval and forecast reliability can be applied also to different failure forecast methods. We applied for the first time the herein presented approach in near real time during the emergency scenario relevant to the reactivation of the La Saxe rockslide, a large mass wasting menacing the population of Courmayeur, northern Italy, and the important European route E25. We show how the application of simplified but robust forecast models can be a convenient method to manage and support early warning systems during critical situations. References: Fukuzono T. (1985), A New Method for Predicting the Failure Time of a Slope, Proc. IVth International Conference and Field Workshop on Landslides, Tokyo.

Role of the Internet in Anticipating and Mitigating Earthquake Catastrophes, and the Emergence of Personal Risk Management (Invited)

NASA Astrophysics Data System (ADS)

Rundle, J. B.; Holliday, J. R.; Donnellan, A.; Graves, W.; Tiampo, K. F.; Klein, W.

2009-12-01

Risks from natural and financial catastrophes are currently managed by a combination of large public and private institutions. Public institutions usually are comprised of government agencies that conduct studies, formulate policies and guidelines, enforce regulations, and make “official” forecasts. Private institutions include insurance and reinsurance companies, and financial service companies that underwrite catastrophe (“cat”) bonds, and make private forecasts. Although decisions about allocating resources and developing solutions are made by large institutions, the costs of dealing with catastrophes generally fall for the most part on businesses and the general public. Information on potential risks is generally available to the public for some hazards but not others. For example, in the case of weather, private forecast services are provided by www.weather.com and www.wunderground.com. For earthquakes in California (only), the official forecast is the WGCEP-USGS forecast, but provided in a format that is difficult for the public to use. Other privately made forecasts are currently available, for example by the JPL QuakeSim and Russian groups, but these efforts are limited. As more of the world’s population moves increasingly into major seismic zones, new strategies are needed to allow individuals to manage their personal risk from large and damaging earthquakes. Examples include individual mitigation measures such as retrofitting, as well as microinsurance in both developing and developed countries, as well as other financial strategies. We argue that the “long tail” of the internet offers an ideal, and greatly underutilized mechanism to reach out to consumers and to provide them with the information and tools they need to confront and manage seismic hazard and risk on an individual, personalized basis. Information of this type includes not only global hazard forecasts, which are now possible, but also global risk estimation. Additionally, social networking tools are available that will allow self-organizing, disaster-resilient communities to arise as emergent structures from the underlying nonlinear social dynamics. In this talk, we argue that the current style of risk management is not making adequate use of modern internet technology, and that significantly more can be done. We suggest several avenues to proceed, in particular making use of the internet for earthquake forecast and information delivery, as well as tracking forecast validation and verification on a real-time basis. We also show examples of forecasts delivered over the internet, and describe how these are made.

CARICOF - The Caribbean Regional Climate Outlook Forum

NASA Astrophysics Data System (ADS)

Van Meerbeeck, Cedric

2013-04-01

Regional Climate Outlook Forums (RCOFs) are viewed as a critical building block in the Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO). The GFCS seeks to extend RCOFs to all vulnerable regions of the world such as the Caribbean, of which the entire population is exposed to water- and heat-related natural hazards. An RCOF is initially intended to identify gaps in information and technical capability; facilitate research cooperation and data exchange within and between regions, and improve coordination within the climate forecasting community. A focus is given on variations in climate conditions on a seasonal timescale. In this view, the relevance of a Caribbean RCOF (CARICOF) is the following: while the seasonality of the climate in the Caribbean has been well documented, major gaps in knowledge exist in terms of the drivers in the shifts of amplitude and phase of seasons (as evidenced from the worst region-wide drought period in recent history during 2009-2010). To address those gaps, CARICOF has brought together National Weather Services (NWSs) from 18 territories under the coordination of the Caribbean Institute for Meteorology and Hydrology (CIMH), to produce region-wide, consensus, seasonal climate outlooks since March 2012. These outlooks include tercile rainfall forecasts, sea and air surface temperature forecasts as well as the likely evolution of the drivers of seasonal climate variability in the region, being amongst others the El Niño Southern Oscillation or tropical Atlantic and Caribbean Sea temperatures. Forecasts for both the national-scale forecasts made by the NWSs and CIMH's regional-scale forecast amalgamate output from several forecasting tools. These currently include: (1) statistical models such as Canonical Correlation Analysis run with the Climate Predictability Tool, providing tercile rainfall forecasts at weather station scale; (2) a global outlooks published by the WMO appointed Global Producing Centres (GPCs). Indications are that the current seasonal forecasting system used by CARICOF has produced reliable outlooks than previously available. Nevertheless, through its forum platform, areas for further development are continuously being defined, which are then implemented through efficient information exchanges between and hands-on training of forecasters. Finally, the disaster research and emergency management communities have shown that effective early warnings of impending hazards need to be complemented by information on the risks actually posed by the hazards and pathways for action. CARICOF is to address this issue by designing the outputs of the seasonal climate outlooks such that they can then effectively feed into an early warning information system of seasonal climate variability related hazards to its constituent countries' and territories major socio-economic sectors.

An Integrated Modeling Framework Forecasting Ecosystem Exposure— A Systems Approach to the Cumulative Impacts of Multiple Stressors

EPA Science Inventory

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change enco...

Small Private Colleges Can Survive

ERIC Educational Resources Information Center

Perkins, Michele

2017-01-01

With high school populations declining throughout New England and in several other regions of the country, it's only natural to be concerned about the fiscal challenges confronting the nation's private colleges and universities. Forecasts by prominent higher education experts increasingly suggest that many, if not most, small private institutions…

76 FR 25409 - Privacy Act of 1974

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-04

... Medicare beneficiaries from CMS databases including: health care usage, demographic, enrollment, and survey... and timely assess the current health care usage by the patient population served by VA, to forecast..., and to understand the numerous implications of cross-usage between VA and non-VA health care systems...

USCG VHF Voice

Science.gov Websites

! Boating Safety Beach Hazards Rip Currents Hypothermia Hurricanes Thunderstorms Lightning Coastal Flooding frequency) The U.S. Coast Guard broadcasts coastal forecasts and storm Warnings of interest to the mariner coverage of coastal U.S., Great Lakes, Hawaii, and populated Alaska coastline. Typical coverage is 20

Summary Report for the Workshop on Integrating Climate Change Adaption into Air Quality Decision Making

EPA Science Inventory

Over the past few decades, air quality planners have forecasted future air pollution levels based on information about changing emissions from stationary and mobile sources, population trends, transportation demand, natural sources of emissions, and other pressures on air quality...

Forecasting natural hazards, performance of scientists, ethics, and the need for transparency

PubMed Central

Guzzetti, Fausto

2016-01-01

Landslides are one of several natural hazards. As other natural hazards, landslides are difficult to predict, and their forecasts are uncertain. The uncertainty depends on the poor understanding of the phenomena that control the slope failures, and on the inherent complexity and chaotic nature of the landslides. This is similar to other natural hazards, including hurricanes, earthquakes, volcanic eruptions, floods, and droughts. Due to the severe impact of landslides on the population, the environment, and the economy, forecasting landslides is of scientific interest and of societal relevance, and scientists attempting to forecast landslides face known and new problems intrinsic to the multifaceted interactions between science, decision-making, and the society. The problems include deciding on the authority and reliability of individual scientists and groups of scientists, and evaluating the performances of individual scientists, research teams, and their institutions. Related problems lay in the increasing subordination of research scientists to politics and decision-makers, and in the conceptual and operational models currently used to organize and pay for research, based on apparently objective criteria and metrics, considering science as any other human endeavor, and favoring science that produces results of direct and immediate application. The paper argues that the consequences of these problems have not been considered fully. PMID:27695154

Forecasting natural hazards, performance of scientists, ethics, and the need for transparency.

PubMed

Guzzetti, Fausto

2016-10-20

Landslides are one of several natural hazards. As other natural hazards, landslides are difficult to predict, and their forecasts are uncertain. The uncertainty depends on the poor understanding of the phenomena that control the slope failures, and on the inherent complexity and chaotic nature of the landslides. This is similar to other natural hazards, including hurricanes, earthquakes, volcanic eruptions, floods, and droughts. Due to the severe impact of landslides on the population, the environment, and the economy, forecasting landslides is of scientific interest and of societal relevance, and scientists attempting to forecast landslides face known and new problems intrinsic to the multifaceted interactions between science, decision-making, and the society. The problems include deciding on the authority and reliability of individual scientists and groups of scientists, and evaluating the performances of individual scientists, research teams, and their institutions. Related problems lay in the increasing subordination of research scientists to politics and decision-makers, and in the conceptual and operational models currently used to organize and pay for research, based on apparently objective criteria and metrics, considering science as any other human endeavor, and favoring science that produces results of direct and immediate application. The paper argues that the consequences of these problems have not been considered fully.

Forecast-based interventions can reduce the health and economic burden of wildfires.

PubMed

Rappold, Ana G; Fann, Neal L; Crooks, James; Huang, Jin; Cascio, Wayne E; Devlin, Robert B; Diaz-Sanchez, David

2014-09-16

We simulated public health forecast-based interventions during a wildfire smoke episode in rural North Carolina to show the potential for use of modeled smoke forecasts toward reducing the health burden and showed a significant economic benefit of reducing exposures. Daily and county wide intervention advisories were designed to occur when fine particulate matter (PM2.5) from smoke, forecasted 24 or 48 h in advance, was expected to exceed a predetermined threshold. Three different thresholds were considered in simulations, each with three different levels of adherence to the advisories. Interventions were simulated in the adult population susceptible to health exacerbations related to the chronic conditions of asthma and congestive heart failure. Associations between Emergency Department (ED) visits for these conditions and daily PM2.5 concentrations under each intervention were evaluated. Triggering interventions at lower PM2.5 thresholds (≤ 20 μg/m(3)) with good compliance yielded the greatest risk reduction. At the highest threshold levels (50 μg/m(3)) interventions were ineffective in reducing health risks at any level of compliance. The economic benefit of effective interventions exceeded $1 M in excess ED visits for asthma and heart failure, $2 M in loss of productivity, $100 K in respiratory conditions in children, and $42 million due to excess mortality.

Particle swarm optimization based artificial neural network model for forecasting groundwater level in Udupi district

NASA Astrophysics Data System (ADS)

Balavalikar, Supreetha; Nayak, Prabhakar; Shenoy, Narayan; Nayak, Krishnamurthy

2018-04-01

The decline in groundwater is a global problem due to increase in population, industries, and environmental aspects such as increase in temperature, decrease in overall rainfall, loss of forests etc. In Udupi district, India, the water source fully depends on the River Swarna for drinking and agriculture purposes. Since the water storage in Bajae dam is declining day-by-day and the people of Udupi district are under immense pressure due to scarcity of drinking water, alternatively depend on ground water. As the groundwater is being heavily used for drinking and agricultural purposes, there is a decline in its water table. Therefore, the groundwater resources must be identified and preserved for human survival. This research proposes a data driven approach for forecasting the groundwater level. The monthly variations in groundwater level and rainfall data in three observation wells located in Brahmavar, Kundapur and Hebri were investigated and the scenarios were examined for 2000-2013. The focus of this research work is to develop an ANN based groundwater level forecasting model and compare with hybrid ANN-PSO forecasting model. The model parameters are tested using different combinations of the data. The results reveal that PSO-ANN based hybrid model gives a better prediction accuracy, than ANN alone.

Futures of global urban expansion: uncertainties and implications for biodiversity conservation

NASA Astrophysics Data System (ADS)

Güneralp, B.; Seto, K. C.

2013-03-01

Urbanization will place significant pressures on biodiversity across the world. However, there are large uncertainties in the amount and location of future urbanization, particularly urban land expansion. Here, we present a global analysis of urban extent circa 2000 and probabilistic forecasts of urban expansion for 2030 near protected areas and in biodiversity hotspots. We estimate that the amount of urban land within 50 km of all protected area boundaries will increase from 450 000 km2 circa 2000 to 1440 000 ± 65 000 km2 in 2030. Our analysis shows that protected areas around the world will experience significant increases in urban land within 50 km of their boundaries. China will experience the largest increase in urban land near protected areas with 304 000 ± 33 000 km2 of new urban land to be developed within 50 km of protected area boundaries. The largest urban expansion in biodiversity hotspots, over 100 000 ± 25 000 km2, is forecasted to occur in South America. Uncertainties in the forecasts of the amount and location of urban land expansion reflect uncertainties in their underlying drivers including urban population and economic growth. The forecasts point to the need to reconcile urban development and biodiversity conservation strategies.

Forecasting infectious disease emergence subject to seasonal forcing.

PubMed

Miller, Paige B; O'Dea, Eamon B; Rohani, Pejman; Drake, John M

2017-09-06

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models. We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic. Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly. Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

Informing policy makers about future health spending: a comparative analysis of forecasting methods in OECD countries.

PubMed

Astolfi, Roberto; Lorenzoni, Luca; Oderkirk, Jillian

2012-09-01

Concerns about health care expenditure growth and its long-term sustainability have risen to the top of the policy agenda in many OECD countries. As continued growth in spending places pressure on government budgets, health services provision and patients' personal finances, policy makers have launched forecasting projects to support policy planning. This comparative analysis reviewed 25 models that were developed for policy analysis in OECD countries by governments, research agencies, academics and international organisations. We observed that the policy questions that need to be addressed drive the choice of forecasting model and the model's specification. By considering both the level of aggregation of the units analysed and the level of detail of health expenditure to be projected, we identified three classes of models: micro, component-based, and macro. Virtually all models account for demographic shifts in the population, while two important influences on health expenditure growth that are the least understood include technological innovation and health-seeking behaviour. The landscape for health forecasting models is dynamic and evolving. Advances in computing technology and increases in data granularity are opening up new possibilities for the generation of system of models which become an on-going decision support tool capable of adapting to new questions as they arise. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

PubMed

Wu, Hua'an; Zeng, Bo; Zhou, Meng

2017-11-15

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

Monitoring and Predicting the African Climate for Food Security

NASA Astrophysics Data System (ADS)

Thiaw, W. M.

2015-12-01

Drought is one of the greatest challenges in Africa due to its impact on access to sanitary water and food. In response to this challenge, the international community has mobilized to develop famine early warning systems (FEWS) to bring safe food and water to populations in need. Over the past several decades, much attention has focused on advance risk planning in agriculture and water. This requires frequent updates of weather and climate outlooks. This paper describes the active role of NOAA's African Desk in FEWS. Emphasis is on the operational products from short and medium range weather forecasts to subseasonal and seasonal outlooks in support of humanitarian relief programs. Tools to provide access to real time weather and climate information to the public are described. These include the downscaling of the U.S. National Multi-model Ensemble (NMME) to improve seasonal forecasts in support of Regional Climate Outlook Forums (RCOFs). The subseasonal time scale has emerged as extremely important to many socio-economic sectors. Drawing from advances in numerical models that can now provide a better representation of the MJO, operational subseasonal forecasts are included in the African Desk product suite. These along with forecasts skill assessment and verifications are discussed. The presentation will also highlight regional hazards outlooks basis for FEWSNET food security outlooks.

Operational flash flood forecasting platform based on grid technology

NASA Astrophysics Data System (ADS)

Thierion, V.; Ayral, P.-A.; Angelini, V.; Sauvagnargues-Lesage, S.; Nativi, S.; Payrastre, O.

2009-04-01

Flash flood events of south of France such as the 8th and 9th September 2002 in the Grand Delta territory caused important economic and human damages. Further to this catastrophic hydrological situation, a reform of flood warning services have been initiated (set in 2006). Thus, this political reform has transformed the 52 existing flood warning services (SAC) in 22 flood forecasting services (SPC), in assigning them territories more hydrological consistent and new effective hydrological forecasting mission. Furthermore, national central service (SCHAPI) has been created to ease this transformation and support local services in their new objectives. New functioning requirements have been identified: - SPC and SCHAPI carry the responsibility to clearly disseminate to public organisms, civil protection actors and population, crucial hydrologic information to better anticipate potential dramatic flood event, - a new effective hydrological forecasting mission to these flood forecasting services seems essential particularly for the flash floods phenomenon. Thus, models improvement and optimization was one of the most critical requirements. Initially dedicated to support forecaster in their monitoring mission, thanks to measuring stations and rainfall radar images analysis, hydrological models have to become more efficient in their capacity to anticipate hydrological situation. Understanding natural phenomenon occuring during flash floods mainly leads present hydrological research. Rather than trying to explain such complex processes, the presented research try to manage the well-known need of computational power and data storage capacities of these services. Since few years, Grid technology appears as a technological revolution in high performance computing (HPC) allowing large-scale resource sharing, computational power using and supporting collaboration across networks. Nowadays, EGEE (Enabling Grids for E-science in Europe) project represents the most important effort in term of grid technology development. This paper presents an operational flash flood forecasting platform which have been developed in the framework of CYCLOPS European project providing one of virtual organizations of EGEE project. This platform has been designed to enable multi-simulations processes to ease forecasting operations of several supervised watersheds on Grand Delta (SPC-GD) territory. Grid technology infrastructure, in providing multiple remote computing elements enables the processing of multiple rainfall scenarios, derived to the original meteorological forecasting transmitted by Meteo-France, and their respective hydrological simulations. First results show that from one forecasting scenario, this new presented approach can permit simulations of more than 200 different scenarios to support forecasters in their aforesaid mission and appears as an efficient hydrological decision-making tool. Although, this system seems operational, model validity has to be confirmed. So, further researches are necessary to improve models core to be more efficient in term of hydrological aspects. Finally, this platform could be an efficient tool for developing others modelling aspects as calibration or data assimilation in real time processing.

Aging and social expenditures in Italy: some issues associated with population projections.

PubMed

Terra Abrami, V

1990-01-01

"After describing the main results of the recent Italian population projections, and some possible consequences...aging may have on social expenditures, this paper focuses on attempts to improve the accuracy of development assumptions, with special regard to natural components. Emphasis is placed on the importance of applying specific methodological tools to define self-explanatory assumptions for fertility and mortality and to produce projections which could be considered, with reasonable limitations, as real forecasts." excerpt

The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change.

PubMed

Valladares, Fernando; Matesanz, Silvia; Guilhaumon, François; Araújo, Miguel B; Balaguer, Luis; Benito-Garzón, Marta; Cornwell, Will; Gianoli, Ernesto; van Kleunen, Mark; Naya, Daniel E; Nicotra, Adrienne B; Poorter, Hendrik; Zavala, Miguel A

2014-11-01

Species are the unit of analysis in many global change and conservation biology studies; however, species are not uniform entities but are composed of different, sometimes locally adapted, populations differing in plasticity. We examined how intraspecific variation in thermal niches and phenotypic plasticity will affect species distributions in a warming climate. We first developed a conceptual model linking plasticity and niche breadth, providing five alternative intraspecific scenarios that are consistent with existing literature. Secondly, we used ecological niche-modeling techniques to quantify the impact of each intraspecific scenario on the distribution of a virtual species across a geographically realistic setting. Finally, we performed an analogous modeling exercise using real data on the climatic niches of different tree provenances. We show that when population differentiation is accounted for and dispersal is restricted, forecasts of species range shifts under climate change are even more pessimistic than those using the conventional assumption of homogeneously high plasticity across a species' range. Suitable population-level data are not available for most species so identifying general patterns of population differentiation could fill this gap. However, the literature review revealed contrasting patterns among species, urging greater levels of integration among empirical, modeling and theoretical research on intraspecific phenotypic variation. © 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

Using a new high resolution regional model for malaria that accounts for population density and surface hydrology to determine sensitivity of malaria risk to climate drivers

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Ermert, Volker; Di Giuseppe, Francesca

2013-04-01

In order to better address the role of population dynamics and surface hydrology in the assessment of malaria risk, a new dynamical disease model been developed at ICTP, known as VECTRI: VECtor borne disease community model of ICTP, TRIeste (VECTRI). The model accounts for the temperature impact on the larvae, parasite and adult vector populations. Local host population density affects the transmission intensity, and the model thus reproduces the differences between peri-urban and rural transmission noted in Africa. A new simple pond model framework represents surface hydrology. The model can be used on with spatial resolutions finer than 10km to resolve individual health districts and thus can be used as a planning tool. Results of the models representation of interannual variability and longer term projections of malaria transmission will be shown for Africa. These will show that the model represents the seasonality and spatial variations of malaria transmission well matching a wide range of survey data of parasite rate and entomological inoculation rate (EIR) from across West and East Africa taken in the period prior to large-scale interventions. The model is used to determine the sensitivity of malaria risk to climate variations, both in rainfall and temperature, and then its use in a prototype forecasting system coupled with ECMWF forecasts will be demonstrated.

Forecasting the effects of fertility control on overabundant ungulates: White-tailed deer in the National Capital Region

USGS Publications Warehouse

Raiho, Ann M.; Hooten, Mevin B.; Bates, Scott; Hobbs, N. Thompson

2015-01-01

Overabundant populations of ungulates have caused environmental degradation and loss of biological diversity in ecosystems throughout the world. Culling or regulated harvest is often used to control overabundant species. These methods are difficult to implement in national parks, other types of conservation reserves, or in residential areas where public hunting may be forbidden by policy. As a result, fertility control has been recommended as a non-lethal alternative for regulating ungulate populations. We evaluate this alternative using white-tailed deer in national parks in the vicinity of Washington, D.C., USA as a model system. Managers seek to reduce densities of white-tailed deer from the current average (50 deer per km2) to decrease harm to native plant communities caused by deer. We present a Bayesian hierarchical model using 13 years of population estimates from 8 national parks in the National Capital Region Network. We offer a novel way to evaluate management actions relative to goals using short term forecasts. Our approach confirms past analyses that fertility control is incapable of rapidly reducing deer abundance. Fertility control can be combined with culling to maintain a population below carrying capacity with a high probability of success. This gives managers confronted with problematic overabundance a framework for implementing management actions with a realistic assessment of uncertainty.

Forecasting domestic water demand in the Haihe river basin under changing environment

NASA Astrophysics Data System (ADS)

Wang, Xiao-Jun; Zhang, Jian-Yun; Shahid, Shamsuddin; Xie, Yu-Xuan; Zhang, Xu

2018-02-01

A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs) namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand in the Haihe River basin under Representative Concentration Pathways (RCPs) 4.5. The results showed that domestic water demand in different sub-basins of the Haihe river basin will gradually increase due to continuous increase of population and rise in temperature. It is projected to increase maximum 136.22 × 108 m3 by GCM BNU-ESM and the minimum 107.25 × 108 m3 by CNRM-CM5 in 2030. In spite of uncertainty in projection, it can be remarked that climate change and population growth would cause increase in water demand and consequently, reduce the gap between water supply and demand, which eventually aggravate the condition of existing water stress in the basin. Water demand management should be emphasized for adaptation to ever increasing water demand and mitigation of the impacts of environmental changes.

Forecasting the Effects of Fertility Control on Overabundant Ungulates: White-Tailed Deer in the National Capital Region.

PubMed

Raiho, Ann M; Hooten, Mevin B; Bates, Scott; Hobbs, N Thompson

2015-01-01

Overabundant populations of ungulates have caused environmental degradation and loss of biological diversity in ecosystems throughout the world. Culling or regulated harvest is often used to control overabundant species. These methods are difficult to implement in national parks, other types of conservation reserves, or in residential areas where public hunting may be forbidden by policy. As a result, fertility control has been recommended as a non-lethal alternative for regulating ungulate populations. We evaluate this alternative using white-tailed deer in national parks in the vicinity of Washington, D.C., USA as a model system. Managers seek to reduce densities of white-tailed deer from the current average (50 deer per km2) to decrease harm to native plant communities caused by deer. We present a Bayesian hierarchical model using 13 years of population estimates from 8 national parks in the National Capital Region Network. We offer a novel way to evaluate management actions relative to goals using short term forecasts. Our approach confirms past analyses that fertility control is incapable of rapidly reducing deer abundance. Fertility control can be combined with culling to maintain a population below carrying capacity with a high probability of success. This gives managers confronted with problematic overabundance a framework for implementing management actions with a realistic assessment of uncertainty.

Branding a State University: Doing It Right

ERIC Educational Resources Information Center

Dholakia, Ruby Roy; Acciardo, Linda A.

2014-01-01

Shrinking financial support from governments and forecast declines in the college-going population have combined to exert tremendous pressure on institutions of higher learning. Branding as a strategy has become more popular as a way of differentiating an institution from its competition, but the complexity of higher education makes branding an…

Simulating the Interactions Among Land Use, Transportation, and Economy to Inform Light Rail Transit Decisions

EPA Science Inventory

In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic a...

Simulating the Interactions Among Land Use, Transportation, and Economy to Inform Light Rail Transit Decisions (proceedings)

EPA Science Inventory

In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic a...

Forecasting the relative influence of environmental and anthropogenic stressors on polar bears

Treesearch

Todd C. Atwood; Bruce G. Marcot; David C. Douglas; Steven C. Amstrup; Karyn D. Rode; George M. Durner; Jeffrey F. Bromaghin

2016-01-01

Effective conservation planning requires understanding and ranking threats to wildlife populations. We developed a Bayesian network model to evaluate the relative influence of environmental and anthropogenic stressors, and their mitigation, on the persistence of polar bears (Ursus maritimus). Overall sea ice conditions, affected by rising global...

Understanding the implications of a changing environment on harvested bivalve populations using habitat suitability models.

EPA Science Inventory

Habitat suitability models are useful to forecast how environmental change may affect the abundance or distribution of species of interest. In the case of harvested bivalves, those models may be used to estimate the vulnerability of this valued ecosystem good to stressors. Using ...

Understanding the implications of a changing environment on harvested bivalve populations using habitat suitability models

EPA Science Inventory

Habitat suitability models are useful to forecast how environmental change may affect the abundance or distribution of species of interest. In the case of harvested bivalves, those models may be used to estimate the vulnerability of this valued ecosystem good to stressors. Using ...

PROTOZOAL INFECTIONS OF THE EASTERN OYSTER (CRASSOSTREA VIRGINICA) IN THE UPPER CHESAPEAKE BAY: A POTENTIAL ECOLOGICAL FORECAST

EPA Science Inventory

Perkinsus marinus and Haplosporidium nelsoni cause devasting infections in populations of the eastern oyster, Crassostrea virginica, along the US Atlantic coast and Gulf of Mexico. Salinity and temperature are considered major controlling factors in the prevalence and infection i...

Grandparent Education for Assisted Living Facilities

ERIC Educational Resources Information Center

Strom, Robert D.; Strom, Paris S.

2017-01-01

The assisted living population is forecast to increase at a rapid rate. Quality of life for residents should be improved by giving greater attention to their cognitive, emotional, and social needs. A university lifespan development team provided a grandparent education course at a large assisted living facility with the assistance of 20 resident…

Probabilistic and spatially variable niches inferred from demography

Treesearch

Jeffrey M. Diez; Itamar Giladi; Robert Warren; H. Ronald Pulliam

2014-01-01

Summary 1. Mismatches between species distributions and habitat suitability are predicted by niche theory and have important implications for forecasting how species may respond to environmental changes. Quantifying these mismatches is challenging, however, due to the high dimensionality of species niches and the large spatial and temporal variability in population...

Cercospora leaf spot: monitoring and managing fungicide resistance in populations of Cercospora beticola in Michigan

USDA-ARS?s Scientific Manuscript database

Cercospora leaf spot (CLS, Cercospora beticola) is the most serious foliar disease of sugarbeet in Michigan and Ontario.Management of CLS depends on timely fungicide applications, disease forecasting prediction models and the use of CLS resistant sugar beet varieties. Fungicides have a dominant role...

FAA Aviation Forecasts - Fiscal Years 1987-1998.

DTIC Science & Technology

1987-02-01

production of turbine powered aircraft. Further, a majority of the companies have significantly reduced their work forces and have consolidated plants . The...Hernando, Hillsborough, Pasco, and Pinellas . The hub’s 1985 population was 1.9 million. By 2000, the hub is expected to reach 2.4 mil- lion, which is 28

Controlling vector-borne disease and adapting to climate change with novel research on disease forecasting to target new vector control materials and technologies

USDA-ARS?s Scientific Manuscript database

Population growth, frontier agricultural expansion, and urbanization transform the landscape and the surrounding ecosystem, affecting climate and interactions between animals and humans, and significantly influencing the transmission dynamics and geographic distribution of malaria, dengue and other ...

Benchmarking an operational procedure for rapid flood mapping and risk assessment in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Salamon, Peter; Kalas, Milan; Bianchi, Alessandra; Feyen, Luc

2016-04-01

The development of real-time methods for rapid flood mapping and risk assessment is crucial to improve emergency response and mitigate flood impacts. This work describes the benchmarking of an operational procedure for rapid flood risk assessment based on the flood predictions issued by the European Flood Awareness System (EFAS). The daily forecasts produced for the major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations, based on the hydro-meteorological dataset of EFAS. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in near real-time in terms of flood prone areas, potential economic damage, affected population, infrastructures and cities. An extensive testing of the operational procedure is carried out using the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-derived flood footprints, while ground-based estimations of economic damage and affected population is compared against modelled estimates. We evaluated the skill of flood hazard and risk estimations derived from EFAS flood forecasts with different lead times and combinations. The assessment includes a comparison of several alternative approaches to produce and present the information content, in order to meet the requests of EFAS users. The tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management.

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools.

PubMed

Seto, Karen C; Güneralp, Burak; Hutyra, Lucy R

2012-10-02

Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km(2), nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr(-1)), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses.

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools

PubMed Central

Seto, Karen C.; Güneralp, Burak; Hutyra, Lucy R.

2012-01-01

Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km2, nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr−1), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses. PMID:22988086

On the dynamics of the world demographic transition and financial-economic crises forecasts

NASA Astrophysics Data System (ADS)

Akaev, A.; Sadovnichy, V.; Korotayev, A.

2012-05-01

The article considers dynamic processes involving non-linear power-law behavior in such apparently diverse spheres, as demographic dynamics and dynamics of prices of highly liquid commodities such as oil and gold. All the respective variables exhibit features of explosive growth containing precursors indicating approaching phase transitions/catastrophes/crises. The first part of the article analyzes mathematical models of demographic dynamics that describe various scenarios of demographic development in the post-phase-transition period, including a model that takes the limitedness of the Earth carrying capacity into account. This model points to a critical point in the early 2050s, when the world population, after reaching its maximum value may decrease afterward stabilizing then at a certain stationary level. The article presents an analysis of the influence of the demographic transition (directly connected with the hyperexponential growth of the world population) on the global socioeconomic and geopolitical development. The second part deals with the phenomenon of explosive growth of prices of such highly liquid commodities as oil and gold. It is demonstrated that at present the respective processes could be regarded as precursors of waves of the global financial-economic crisis that will demand the change of the current global economic and political system. It is also shown that the moments of the start of the first and second waves of the current global crisis could have been forecasted with a model of accelerating log-periodic fluctuations superimposed over a power-law trend with a finite singularity developed by Didier Sornette and collaborators. With respect to the oil prices, it is shown that it was possible to forecast the 2008 crisis with a precision up to a month already in 2007. The gold price dynamics was used to calculate the possible time of the start of the second wave of the global crisis (July-August 2011); note that this forecast has turned out to be quite correct.

Intervening to reduce the future burden of occupational cancer in britain: what could work?

PubMed

Hutchings, Sally; Cherrie, John W; Van Tongeren, Martie; Rushton, Lesley

2012-10-01

In Britain, 14 carcinogenic agents and occupational circumstances currently account for 86% of estimated occupation attributable cancer. The future burden associated with these carcinogens has been forecast, using attributable fractions for forecast scenarios representing patterns of past and predicted future exposure, and exposure levels representing the introduction of new occupational exposure limits, increased levels of compliance with these limits and other reductions in worker exposure. Without intervention, occupational attributable cancers are forecast to remain at more than 10,000 by 2060. With modest intervention over 2,600, or with stricter interventions more than 8,200 cancers could be avoided by 2060 although because of long latency no impact will be seen until at least 10 years after intervention. Effective interventions assessed in this study include reducing workplace exposure limits and improving compliance with these limits. Cancers associated with asbestos, diesel engine exhaust, polycyclic aromatic hydrocarbons, work as a painter, radon, and solar radiation are forecast to continue, with construction remaining the prime industry of concern. Although exposure levels to the established carcinogens are falling, workers are remaining exposed at low levels at which there is still a cancer risk, although the aging population also contributes to rising cancer numbers, These forecasts can be used to assess the relative costs to society of different occupational carcinogenic agents, and the relative merits and savings associated with alternative intervention strategies. The methods are adaptable for different data circumstances, other types of interventions and could be extended to environmental carcinogens and other chronic diseases.

Sensitivity of WRF-chem predictions to dust source function specification in West Asia

NASA Astrophysics Data System (ADS)

Nabavi, Seyed Omid; Haimberger, Leopold; Samimi, Cyrus

2017-02-01

Dust storms tend to form in sparsely populated areas covered by only few observations. Dust source maps, known as source functions, are used in dust models to allocate a certain potential of dust release to each place. Recent research showed that the well known Ginoux source function (GSF), currently used in Weather Research and Forecasting Model coupled with Chemistry (WRF-chem), exhibits large errors over some regions in West Asia, particularly near the IRAQ/Syrian border. This study aims to improve the specification of this critical part of dust forecasts. A new source function based on multi-year analysis of satellite observations, called West Asia source function (WASF), is therefore proposed to raise the quality of WRF-chem predictions in the region. WASF has been implemented in three dust schemes of WRF-chem. Remotely sensed and ground-based observations have been used to verify the horizontal and vertical extent and location of simulated dust clouds. Results indicate that WRF-chem performance is significantly improved in many areas after the implementation of WASF. The modified runs (long term simulations over the summers 2008-2012, using nudging) have yielded an average increase of Spearman correlation between observed and forecast aerosol optical thickness by 12-16 percent points compared to control runs with standard source functions. They even outperform MACC and DREAM dust simulations over many dust source regions. However, the quality of the forecasts decreased with distance from sources, probably due to deficiencies in the transport and deposition characteristics of the forecast model in these areas.

Projections of global health outcomes from 2005 to 2060 using the International Futures integrated forecasting model

PubMed Central

Hughes, Barry B; Peterson, Cecilia M; Rothman, Dale S; Solórzano, José R; Mathers, Colin D; Dickson, Janet R

2011-01-01

Abstract Objective To develop an integrated health forecasting model as part of the International Futures (IFs) modelling system. Methods The IFs model begins with the historical relationships between economic and social development and cause-specific mortality used by the Global Burden of Disease project but builds forecasts from endogenous projections of these drivers by incorporating forward linkages from health outcomes back to inputs like population and economic growth. The hybrid IFs system adds alternative structural formulations for causes not well served by regression models and accounts for changes in proximate health risk factors. Forecasts are made to 2100 but findings are reported to 2060. Findings The base model projects that deaths from communicable diseases (CDs) will decline by 50%, whereas deaths from both non-communicable diseases (NCDs) and injuries will more than double. Considerable cross-national convergence in life expectancy will occur. Climate-induced fluctuations in agricultural yield will cause little excess childhood mortality from CDs, although other climate−health pathways were not explored. An optimistic scenario will produce 39 million fewer deaths in 2060 than a pessimistic one. Our forward linkage model suggests that an optimistic scenario would result in a 20% per cent increase in gross domestic product (GDP) per capita, despite one billion additional people. Southern Asia would experience the greatest relative mortality reduction and the largest resulting benefit in per capita GDP. Conclusion Long-term, integrated health forecasting helps us understand the links between health and other markers of human progress and offers powerful insight into key points of leverage for future improvements. PMID:21734761

Projections of global health outcomes from 2005 to 2060 using the International Futures integrated forecasting model.

PubMed

Hughes, Barry B; Kuhn, Randall; Peterson, Cecilia M; Rothman, Dale S; Solórzano, José R; Mathers, Colin D; Dickson, Janet R

2011-07-01

To develop an integrated health forecasting model as part of the International Futures (IFs) modelling system. The IFs model begins with the historical relationships between economic and social development and cause-specific mortality used by the Global Burden of Disease project but builds forecasts from endogenous projections of these drivers by incorporating forward linkages from health outcomes back to inputs like population and economic growth. The hybrid IFs system adds alternative structural formulations for causes not well served by regression models and accounts for changes in proximate health risk factors. Forecasts are made to 2100 but findings are reported to 2060. The base model projects that deaths from communicable diseases (CDs) will decline by 50%, whereas deaths from both non-communicable diseases (NCDs) and injuries will more than double. Considerable cross-national convergence in life expectancy will occur. Climate-induced fluctuations in agricultural yield will cause little excess childhood mortality from CDs, although other climate-health pathways were not explored. An optimistic scenario will produce 39 million fewer deaths in 2060 than a pessimistic one. Our forward linkage model suggests that an optimistic scenario would result in a 20% per cent increase in gross domestic product (GDP) per capita, despite one billion additional people. Southern Asia would experience the greatest relative mortality reduction and the largest resulting benefit in per capita GDP. Long-term, integrated health forecasting helps us understand the links between health and other markers of human progress and offers powerful insight into key points of leverage for future improvements.

Hydrologic Forecasting in the 21st Century: Challenges and Directions of Research

NASA Astrophysics Data System (ADS)

Restrepo, P.; Schaake, J.

2009-04-01

Traditionally, the role of the Hydrology program of the National Weather Service has been centered around forecasting floods, in order to minimize loss of lives and damage to property as a result of floods as well as water levels for navigable rivers, and water supply in some areas of the country. A number of factors, including shifting population patterns, widespread drought and concerns about climate change have made it imperative to widen the focus to cover forecasting flows ranging from drought to floods and anything in between. Because of these concerns, it is imperative to develop models that rely more on the physical characteristics of the watershed for parameterization and less on historical observations. Furthermore, it is also critical to consider explicitly the sources of uncertainty in the forecasting process, including parameter values, model structure, forcings (both observations and forecasts), initial conditions, and streamflow observations. A consequence of more widespread occurrence of low flows as a result either of the already evident earlier snowmelt in the Western United States, or of the predicted changes in precipitation patterns, is the issue of water quality: lower flows will have higher concentrations of certain pollutants. This paper describes the current projects and future directions of research for hydrologic forecasting in the United States. Ongoing projects on quantitative precipitation and temperature estimates and forecasts, uncertainty modeling by the use of ensembles, data assimilation, verification, distributed conceptual modeling will be reviewed. Broad goals of the research directions are: 1) reliable modeling of the different sources of uncertainty. 2) a more expeditious and cost-effective approach by reducing the effort required in model calibration; 3) improvements in forecast lead-time and accuracy; 4) an approach for rapid adjustment of model parameters to account for changes in the watershed, both rapid as the result from forest fires or levee breaches, and slow, as the result of watershed reforestation, reforestation or urban development; 5) an expanded suite of products, including soil moisture and temperature forecasts, and water quality constituents; and 6) a comprehensive verification system to assess the effectiveness of the other 5 goals. To this end, the research plan places an emphasis on research of models with parameters that can be derived from physical watershed characteristics. Purely physically based models may be unattainable or impractical, and, therefore, models resulting from a combination of physically and conceptually approached processes may be required With respect to the hydrometeorological forcings the research plan emphasizes the development of improved precipitation estimation techniques through the synthesis of radar, rain gauge, satellite, and numerical weather prediction model output, particularly in those areas where ground-based sensors are inadequate to detect spatial variability in precipitation. Better estimation and forecasting of precipitation are most likely to be achieved by statistical merging of remote-sensor observations and forecasts from high-resolution numerical prediction models. Enhancements to the satellite-based precipitation products will include use of TRMM precipitation data in preparation for information to be supplied by the Global Precipitation Mission satellites not yet deployed. Because of a growing need for services in water resources, including low-flow forecasts for water supply customers, we will be directing research into coupled surface-groundwater models that will eventually replace the groundwater component of the existing models, and will be part of the new generation of models. Finally, the research plan covers the directions of research for probabilistic forecasting using ensembles, data assimilation and the verification and validation of both deterministic and probabilistic forecasts.

Need and reasons for population control.

PubMed

Srivastava, P K

1995-02-01

The author posits that overpopulation is a problem that can be solved only with an understanding of the natural environment and its interactions with people. The formation of a national Indian planning commission for population control is viewed as necessary for motivating all people to reduce population size. More statistics are needed in India on environmental measures and population. Man is part of nature, but not a master of nature. Water-borne diseases occur after flooding and earthquakes and when the drinking water supply is contaminated. Weather forecasting would be useful in preventing these diseases. Construction sites are reservoirs of infectious diseases and affect the natural environment. Environmental conditions must be monitored. Monitoring of environmental sanitation will help in forecasting epidemics and in curbing the negative affects on human physical, mental, and social well-being. Man alters the environment through such activities as urbanization, industrialization, deforestation, construction of dams and irrigation channels, and use of pesticides and chemical fertilizers. New negative effects are occurring. Environmental degradation lowers economic status, which in turn contributes to poverty. Low income populations are more prone to crime, drug abuse, alcoholism, depression, and violence. The health status of an individual, community, or nation is a product of man's internal environment and the external natural environment, which can be physical, biological, and social. The biological environment includes viruses and disease producing agents, which struggle for their survival. The social environment includes customs, values, socioeconomic status, religion, standard of living, and political factors. Natural ecological regulation was suspended when man made technological advances. Scientific knowledge does not yet hold all the answers. Pollution is the result of progress in technology. Agricultural development has negative effects on the environment. Large population size contributes to the contamination of air, water, and agricultural areas. Demographic transition has been uneven in India.

The PRESSCA operational early warning system for landslide forecasting: the 11-12 November 2013 rainfall event in Central Italy.

NASA Astrophysics Data System (ADS)

Ciabatta, Luca; Brocca, Luca; Ponziani, Francesco; Berni, Nicola; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Umbria Region, located in Central Italy, is one of the most landslide risk prone area in Italy, almost yearly affected by landslides events at different spatial scales. For early warning procedures aimed at the assessment of the hydrogeological risk, the rainfall thresholds represent the main tool for the Italian Civil Protection System. As shown in previous studies, soil moisture plays a key-role in landslides triggering. In fact, acting on the pore water pressure, soil moisture influences the rainfall amount needed for activating a landslide. In this work, an operational physically-based early warning system, named PRESSCA, that takes into account soil moisture for the definition of rainfall thresholds is presented. Specifically, the soil moisture conditions are evaluated in PRESSCA by using a distributed soil water balance model that is recently coupled with near real-time satellite soil moisture product obtained from ASCAT (Advanced SCATterometer) and from in-situ monitoring data. The integration of three different sources of soil moisture information allows to estimate the most accurate possible soil moisture condition. Then, both observed and forecasted rainfall data are compared with the soil moisture-based thresholds in order to obtain risk indicators over a grid of ~ 5 km. These indicators are then used for the daily hydrogeological risk evaluation and management by the Civil Protection regional service, through the sharing/delivering of near real-time landslide risk scenarios (also through an open source web platform: www.cfumbria.it). On the 11th-12th November, 2013, Umbria Region was hit by an exceptional rainfall event with up to 430mm/72hours that resulted in significant economic damages, but fortunately no casualties among the population. In this study, the results during the rainfall event of PRESSCA system are described, by underlining the model capability to reproduce, two days in advance, landslide risk scenarios in good spatial and temporal agreement with the occurred actual conditions. High-resolution risk scenarios (100mx100m), obtained by coupling PRESSCA forecasts with susceptibility and vulnerability layers, are also produced. The results show good relationship between the PRESSCA forecast and the reported landslides to the Civil Protection Service during the rainfall event, confirming the system robustness. The good forecasts of PRESSCA system have surely contributed to start well in advance the Civil Protection operations (alerting local authorities and population).

A Machine LearningFramework to Forecast Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in computational expense. The low computational cost (and by association low computer-power requirement) means that the machine learning algorithms could be installed on a wave-energy converter as a form of "edge computing" where a device could forecast its own 48-hour energy production.

Chilling and heat requirements for leaf unfolding in European beech and sessile oak populations at the southern limit of their distribution range.

PubMed

Dantec, Cécile F; Vitasse, Yann; Bonhomme, Marc; Louvet, Jean-Marc; Kremer, Antoine; Delzon, Sylvain

2014-11-01

With global warming, an advance in spring leaf phenology has been reported worldwide. However, it is difficult to forecast phenology for a given species, due to a lack of knowledge about chilling requirements. We quantified chilling and heat requirements for leaf unfolding in two European tree species and investigated their relative contributions to phenological variations between and within populations. We used an extensive database containing information about the leaf phenology of 14 oak and 10 beech populations monitored over elevation gradients since 2005. In parallel, we studied the various bud dormancy phases, in controlled conditions, by regularly sampling low- and high-elevation populations during fall and winter. Oak was 2.3 times more sensitive to temperature for leaf unfolding over the elevation gradient and had a lower chilling requirement for dormancy release than beech. We found that chilling is currently insufficient for the full release of dormancy, for both species, at the lowest elevations in the area studied. Genetic variation in leaf unfolding timing between and within oak populations was probably due to differences in heat requirement rather than differences in chilling requirement. Our results demonstrate the importance of chilling for leaf unfolding in forest trees and indicate that the advance in leaf unfolding phenology with increasing temperature will probably be less pronounced than forecasted. This highlights the urgent need to determine experimentally the interactions between chilling and heat requirements in forest tree species, to improve our understanding and modeling of changes in phenological timing under global warming.

Habitat Fragmentation in Arid Zones: A Case Study of Linaria nigricans Under Land Use Changes (SE Spain)

NASA Astrophysics Data System (ADS)

Peñas, Julio; Benito, Blas; Lorite, Juan; Ballesteros, Miguel; Cañadas, Eva María; Martinez-Ortega, Montserrat

2011-07-01

Habitat fragmentation due to human activities is one of the most important causes of biodiversity loss. In Mediterranean areas the species have co-evolved with traditional farming, which has recently been replaced for more severe and aggressive practices. We use a methodological approach that enables the evaluation of the impact that agriculture and land use changes have for the conservation of sensitive species. As model species, we selected Linaria nigricans, a critically endangered plant from arid and semiarid ecosystems in south-eastern Spain. A chronosequence of the evolution of the suitable habitat for the species over more than 50 years has been reconstructed and several geometrical fragmentation indices have been calculated. A new index called fragmentation cadence (FC) is proposed to quantify the historical evolution of habitat fragmentation regardless of the habitat size. The application of this index has provided objective forecasting of the changes of each remnant population of L. nigricans. The results indicate that greenhouses and construction activities (mainly for tourist purposes) exert a strong impact on the populations of this endangered species. The habitat depletion showed peaks that constitute the destruction of 85% of the initial area in only 20 years for some populations of L. nigricans. According to the forecast established by the model, a rapid extinction could take place and some populations may disappear as early as the year 2030. Fragmentation-cadence analysis can help identify population units of primary concern for its conservation, by means of the adoption of improved management and regulatory measures.

Forecasting Financial Resources for Future Traumatic Spinal Cord Injury Care Using Simulation Modeling.

PubMed

Ahn, Henry; Lewis, Rachel; Santos, Argelio; Cheng, Christiana L; Noonan, Vanessa K; Dvorak, Marcel F; Singh, Anoushka; Linassi, A Gary; Christie, Sean; Goytan, Michael; Atkins, Derek

2017-10-15

Survivors of traumatic spinal cord injury (tSCI) have intense healthcare needs during acute and rehabilitation care and often through the rest of life. To prepare for a growing and aging population, simulation modeling was used to forecast the change in healthcare financial resources and long-term patient outcomes between 2012 and 2032. The model was developed with data from acute and rehabilitation care facilities across Canada participating in the Access to Care and Timing project. Future population and tSCI incidence for 2012 and 2032 were predicted with data from Statistics Canada and the Canadian Institute for Health Information. The projected tSCI incidence for 2012 was validated with actual data from the Rick Hansen SCI Registry of the participating facilities. Using a medium growth scenario, in 2032, the projected median age of persons with tSCI is 57 and persons 61 and older will account for 46% of injuries. Admissions to acute and rehabilitation facilities in 2032 were projected to increase by 31% and 25%, respectively. Because of the demographic shift to an older population, an increase in total population life expectancy with tSCI of 13% was observed despite a 22% increase in total life years lost to tSCI between 2012 and 2032. Care cost increased 54%, and rest of life cost increased 37% in 2032, translating to an additional CAD $16.4 million. With the demographics and management of tSCI changing with an aging population, accurate projections for the increased demand on resources will be critical for decision makers when planning the delivery of healthcare after tSCI.

The use of seasonal forecasts in a crop failure early warning system for West Africa

NASA Astrophysics Data System (ADS)

Nicklin, K. J.; Challinor, A.; Tompkins, A.

2011-12-01

Seasonal rainfall in semi-arid West Africa is highly variable. Farming systems in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield forecasting system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate forecasts may be able to increase the lead-time of yield forecasts and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic seasonal forecasts and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather forecasts (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the system is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the forecast date, followed by ECMWF system 3 seasonal forecasts until harvest. The variation of skill with forecast date is assessed along with the extent to which forecasts can be improved by bias correction of the rainfall data. Two forms of bias correction are applied: a novel method of spatially bias correcting daily data, and statistical bias correction of the frequency and intensity distribution. Results are presented using both observed yields and the control run as the reference for verification. The potential for current dynamic seasonal forecasts to form part of an operational system giving timely and accurate warnings of crop failure is discussed. Traore S.B. et al., 2006. A Review of Agrometeorological Monitoring Tools and Methods Used in the West African Sahel. In: Motha R.P. et al., Strengthening Operational Agrometeorological Services at the National Level. Technical Bulletin WAOB-2006-1 and AGM-9, WMO/TD No. 1277. Pages 209-220. www.wamis.org/agm/pubs/agm9/WMO-TD1277.pdf Challinor A.J. et al., 2004. Design and optimisation of a large-area process based model for annual crops. Agric. For. Meteorol. 124, 99-120.

Forecasting gypsy moth egg-mass density

Treesearch

Robert W. Campbell; Robert W. Campbell

1973-01-01

Several multiple regression models for gypsy moth egg-mass density were developed from data accumulated in eastern New England between 1911 and 1931. Analysis of these models indicates that: (1) The gypsy moth population system was relatively stable in either the OUTBREAK phase or the INNOCUOUS one; (2) Several naturally occurring processes that could terminate the...

Predicting fecundity of fathead minnows (Pimephales promelas) exposed toeEndocrine-disrupting chemicals using a MATLAB®-based model of oocyte growth dynamics

EPA Science Inventory

Fish spawning is often used as an integrated measure of reproductive toxicity, and an indicator of aquatic ecosystem health in the context of forecasting potential population-level effects considered important for ecological risk assessment. Consequently, there is a need for fle...

Multiple drivers, scales, and interactions influence southern Appalachian stream salamander occupancy

Treesearch

Kristen K. Cecala; John C. Maerz; Brian J. Halstead; John R. Frisch; Ted L. Gragson; Jeffrey Hepinstall-Cymerman; David S. Leigh; C. Rhett Jackson; James T. Peterson; Catherine M. Pringle

2018-01-01

Understanding how factors that vary in spatial scale relate to population abundance is vital to forecasting species responses to environmental change. Stream and river ecosystems are inherently hierarchical, potentially resulting in organismal responses to fine‐scale changes in patch characteristics that are conditional on the watershed context. Here, we...

78 FR 28012 - Tier One Environmental Impact Statement for the Rochester, Minnesota to Twin Cities, Minnesota...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-13

... detail. Project Background and Study Area: Based upon travel demand and growth between the two regional... corridors in the region had been established. The number of jobs currently supported by Rochester employers... transportation alternative that will meet forecasted population and economic growth mobility demands in the...

Cross-National Trends in Mortality Rates among the Elderly.

ERIC Educational Resources Information Center

Myers, GeorgeC.

1978-01-01

An examination of death rates among the elderly and trends over the period 1950-1975 and 1970-1975 for selected developed nations provides evidence of continued strong mortality declines for females and somewhat mixed results for males. Implications of these trends for forecasting the mortality component of U.S. population projections are…

The effects of temperature on nest predation by mammals, birds, and snakes

Treesearch

W. Andrew Cox; F.R. Thompson III; J.L. Reidy

2013-01-01

Understanding how weather influences survival and reproduction is an important component of forecasting how climate change will influence wildlife population viability. Nest predation is the primary source of reproductive failure for passerine birds and can change in response to temperature. However, it is unclear which predator species are responsible for such...

Plant water relations II: how plants manage water deficit and why it matters

USDA-ARS?s Scientific Manuscript database

The availability of fresh water is possibly the greatest limitation to our ability to feed the growing human population (9 billion people forecast by 2050 and 11 billion by 2100). This Teaching Tool examines why water is so critical for plant growth and particularly their food production (primarily ...

MULTIREGION: a simulation-forecasting model of BEA economic area population and employment. [Bureau of Economic Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olsen, R.J.; Westley, G.W.; Herzog, H.W. Jr.

This report documents the development of MULTIREGION, a computer model of regional and interregional socio-economic development. The MULTIREGION model interprets the economy of each BEA economic area as a labor market, measures all activity in terms of people as members of the population (labor supply) or as employees (labor demand), and simultaneously simulates or forecasts the demands and supplies of labor in all BEA economic areas at five-year intervals. In general the outputs of MULTIREGION are intended to resemble those of the Water Resource Council's OBERS projections and to be put to similar planning and analysis purposes. This report hasmore » been written at two levels to serve the needs of multiple audiences. The body of the report serves as a fairly nontechnical overview of the entire MULTIREGION project; a series of technical appendixes provide detailed descriptions of the background empirical studies of births, deaths, migration, labor force participation, natural resource employment, manufacturing employment location, and local service employment used to construct the model.« less

Ice and AIS: ship speed data and sea ice forecasts in the Baltic Sea

NASA Astrophysics Data System (ADS)

Löptien, U.; Axell, L.

2014-07-01

The Baltic Sea is a seasonally ice covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, several ice properties are allocated, but their actual usefulness is difficult to measure and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the Automatic Identification System (AIS), with the respective forecasted ice conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62-67% of ship speed variations can be explained by the forecasted ice properties when fitting a mixed effect model. This statistical fit is based on a test region in the Bothnian Bay during the severe winter 2011 and employes 15 to 25 min averages of ship speed.

Stakeholder-driven geospatial modeling for assessing tsunami vertical-evacuation strategies in the U.S. Pacific Northwest

NASA Astrophysics Data System (ADS)

Wood, N. J.; Schmidtlein, M.; Schelling, J.; Jones, J.; Ng, P.

2012-12-01

Recent tsunami disasters, such as the 2010 Chilean and 2011 Tohoku events, demonstrate the significant life loss that can occur from tsunamis. Many coastal communities in the world are threatened by near-field tsunami hazards that may inundate low-lying areas only minutes after a tsunami begins. Geospatial integration of demographic data and hazard zones has identified potential impacts on populations in communities susceptible to near-field tsunami threats. Pedestrian-evacuation models build on these geospatial analyses to determine if individuals in tsunami-prone areas will have sufficient time to reach high ground before tsunami-wave arrival. Areas where successful evacuations are unlikely may warrant vertical-evacuation (VE) strategies, such as berms or structures designed to aid evacuation. The decision of whether and where VE strategies are warranted is complex. Such decisions require an interdisciplinary understanding of tsunami hazards, land cover conditions, demography, community vulnerability, pedestrian-evacuation models, land-use and emergency-management policy, and decision science. Engagement with the at-risk population and local emergency managers in VE planning discussions is critical because resulting strategies include permanent structures within a community and their local ownership helps ensure long-term success. We present a summary of an interdisciplinary approach to assess VE options in communities along the southwest Washington coast (U.S.A.) that are threatened by near-field tsunami hazards generated by Cascadia subduction zone earthquakes. Pedestrian-evacuation models based on an anisotropic approach that uses path-distance algorithms were merged with population data to forecast the distribution of at-risk individuals within several communities as a function of travel time to safe locations. A series of community-based workshops helped identify potential VE options in these communities, collectively known as "Project Safe Haven" at the State of Washington Emergency Management Division. Models of the influence of stakeholder-driven VE options identified changes in the type and distribution of at-risk individuals. Insights from VE use and performance as an aid to evacuations from the 2011 Tohoku tsunami helped to inform the meetings and the analysis. We developed geospatial tools to automate parts of the pedestrian-evacuation models to support the iterative process of developing VE options and forecasting changes in population exposure. Our summary presents the interdisciplinary effort to forecast population impacts from near-field tsunami threats and to develop effective VE strategies to minimize fatalities in future events.

Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review

PubMed Central

Mosleh, Mostafa K.; Hassan, Quazi K.; Chowdhury, Ehsan H.

2015-01-01

Rice is one of the staple foods for more than three billion people worldwide. Rice paddies accounted for approximately 11.5% of the World's arable land area during 2012. Rice provided ∼19% of the global dietary energy in recent times and its annual average consumption per capita was ∼65 kg during 2010–2011. Therefore, rice area mapping and forecasting its production is important for food security, where demands often exceed production due to an ever increasing population. Timely and accurate estimation of rice areas and forecasting its production can provide invaluable information for governments, planners, and decision makers in formulating policies in regard to import/export in the event of shortfall and/or surplus. The aim of this paper was to review the applicability of the remote sensing-based imagery for rice area mapping and forecasting its production. Recent advances on the resolutions (i.e., spectral, spatial, radiometric, and temporal) and availability of remote sensing imagery have allowed us timely collection of information on the growth and development stages of the rice crop. For elaborative understanding of the application of remote sensing sensors, following issues were described: the rice area mapping and forecasting its production using optical and microwave imagery, synergy between remote sensing-based methods and other developments, and their implications as an operational one. The overview of the studies to date indicated that remote sensing-based methods using optical and microwave imagery found to be encouraging. However, there were having some limitations, such as: (i) optical remote sensing imagery had relatively low spatial resolution led to inaccurate estimation of rice areas; and (ii) radar imagery would suffer from speckles, which potentially would degrade the quality of the images; and also the brightness of the backscatters were sensitive to the interacting surface. In addition, most of the methods used in forecasting rice yield were empirical in nature, so thus it would require further calibration and validation prior to implement over other geographical locations. PMID:25569753

Parcel-scale urban coastal flood mapping: Leveraging the multi-scale CoSMoS model for coastal flood forecasting

NASA Astrophysics Data System (ADS)

Gallien, T.; Barnard, P. L.; Sanders, B. F.

2011-12-01

California coastal sea levels are projected to rise 1-1.4 meters in the next century and evidence suggests mean tidal range, and consequently, mean high water (MHW) is increasing along portions of Southern California Bight. Furthermore, emerging research indicates wind stress patterns associated with the Pacific Decadal Oscillation (PDO) have suppressed sea level rise rates along the West Coast since 1980, and a reversal in this pattern would result in the resumption of regional sea level rise rates equivalent to or exceeding global mean sea level rise rates, thereby enhancing coastal flooding. Newport Beach is a highly developed, densely populated lowland along the Southern California coast currently subject to episodic flooding from coincident high tides and waves, and the frequency and intensity of flooding is expected to increase with projected future sea levels. Adaptation to elevated sea levels will require flood mapping and forecasting tools that are sensitive to the dominant factors affecting flooding including extreme high tides, waves and flood control infrastructure. Considerable effort has been focused on the development of nowcast and forecast systems including Scripps Institute of Oceanography's Coastal Data Information Program (CDIP) and the USGS Multi-hazard model, the Southern California Coastal Storm Modeling System (CoSMoS). However, fine scale local embayment dynamics and overtopping flows are needed to map unsteady flooding effects in coastal lowlands protected by dunes, levees and seawalls. Here, a recently developed two dimensional Godunov non-linear shallow water solver is coupled to water level and wave forecasts from the CoSMoS model to investigate the roles of tides, waves, sea level changes and flood control infrastructure in accurate flood mapping and forecasting. The results of this study highlight the important roles of topographic data, embayment hydrodynamics, water level uncertainties and critical flood processes required for meaningful prediction of sea level rise impacts and coastal flood forecasting.

Application of remote sensors in mapping rice area and forecasting its production: a review.

PubMed

Mosleh, Mostafa K; Hassan, Quazi K; Chowdhury, Ehsan H

2015-01-05

Rice is one of the staple foods for more than three billion people worldwide. Rice paddies accounted for approximately 11.5% of the World's arable land area during 2012. Rice provided ~19% of the global dietary energy in recent times and its annual average consumption per capita was ~65 kg during 2010-2011. Therefore, rice area mapping and forecasting its production is important for food security, where demands often exceed production due to an ever increasing population. Timely and accurate estimation of rice areas and forecasting its production can provide invaluable information for governments, planners, and decision makers in formulating policies in regard to import/export in the event of shortfall and/or surplus. The aim of this paper was to review the applicability of the remote sensing-based imagery for rice area mapping and forecasting its production. Recent advances on the resolutions (i.e., spectral, spatial, radiometric, and temporal) and availability of remote sensing imagery have allowed us timely collection of information on the growth and development stages of the rice crop. For elaborative understanding of the application of remote sensing sensors, following issues were described: the rice area mapping and forecasting its production using optical and microwave imagery, synergy between remote sensing-based methods and other developments, and their implications as an operational one. The overview of the studies to date indicated that remote sensing-based methods using optical and microwave imagery found to be encouraging. However, there were having some limitations, such as: (i) optical remote sensing imagery had relatively low spatial resolution led to inaccurate estimation of rice areas; and (ii) radar imagery would suffer from speckles, which potentially would degrade the quality of the images; and also the brightness of the backscatters were sensitive to the interacting surface. In addition, most of the methods used in forecasting rice yield were empirical in nature, so thus it would require further calibration and validation prior to implement over other geographical locations.

Forecasting the brittle failure of heterogeneous, porous geomaterials

NASA Astrophysics Data System (ADS)

Vasseur, Jérémie; Wadsworth, Fabian; Heap, Michael; Main, Ian; Lavallée, Yan; Dingwell, Donald

2017-04-01

Heterogeneity develops in magmas during ascent and is dominated by the development of crystal and importantly, bubble populations or pore-network clusters which grow, interact, localize, coalesce, outgas and resorb. Pore-scale heterogeneity is also ubiquitous in sedimentary basin fill during diagenesis. As a first step, we construct numerical simulations in 3D in which randomly generated heterogeneous and polydisperse spheres are placed in volumes and which are permitted to overlap with one another, designed to represent the random growth and interaction of bubbles in a liquid volume. We use these simulated geometries to show that statistical predictions of the inter-bubble lengthscales and evolving bubble surface area or cluster densities can be made based on fundamental percolation theory. As a second step, we take a range of well constrained random heterogeneous rock samples including sandstones, andesites, synthetic partially sintered glass bead samples, and intact glass samples and subject them to a variety of stress loading conditions at a range of temperatures until failure. We record in real time the evolution of the number of acoustic events that precede failure and show that in all scenarios, the acoustic event rate accelerates toward failure, consistent with previous findings. Applying tools designed to forecast the failure time based on these precursory signals, we constrain the absolute error on the forecast time. We find that for all sample types, the error associated with an accurate forecast of failure scales non-linearly with the lengthscale between the pore clusters in the material. Moreover, using a simple micromechanical model for the deformation of porous elastic bodies, we show that the ratio between the equilibrium sub-critical crack length emanating from the pore clusters relative to the inter-pore lengthscale, provides a scaling for the error on forecast accuracy. Thus for the first time we provide a potential quantitative correction for forecasting the failure of porous brittle solids that build the Earth's crust.

Forecasting the Incidence and Prevalence of Patients with End-Stage Renal Disease in Malaysia up to the Year 2040

PubMed Central

Adnan, Tassha Hilda; Hashim, Nadiah Hanis; Mohan, Kirubashni; Kim Liong, Ang; Ahmad, Ghazali; Bak Leong, Goh; Bavanandan, Sunita; Haniff, Jamaiyah

2017-01-01

Background. The incidence of patients with end-stage renal disease (ESRD) requiring dialysis has been growing rapidly in Malaysia from 18 per million population (pmp) in 1993 to 231 pmp in 2013. Objective. To forecast the incidence and prevalence of ESRD patients who will require dialysis treatment in Malaysia until 2040. Methodology. Univariate forecasting models using the number of new and current dialysis patients, by the Malaysian Dialysis and Transplant Registry from 1993 to 2013 were used. Four forecasting models were evaluated, and the model with the smallest error was selected for the prediction. Result. ARIMA (0, 2, 1) modeling with the lowest error was selected to predict both the incidence (RMSE = 135.50, MAPE = 2.85, and MAE = 87.71) and the prevalence (RMSE = 158.79, MAPE = 1.29, and MAE = 117.21) of dialysis patients. The estimated incidences of new dialysis patients in 2020 and 2040 are 10,208 and 19,418 cases, respectively, while the estimated prevalence is 51,269 and 106,249 cases. Conclusion. The growth of ESRD patients on dialysis in Malaysia can be expected to continue at an alarming rate. Effective steps to address and curb further increase in new patients requiring dialysis are urgently needed, in order to mitigate the expected financial and health catastrophes associated with the projected increase of such patients. PMID:28348890

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption

PubMed Central

Wu, Hua’an; Zhou, Meng

2017-01-01

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy. PMID:29140266

Toward Seasonal Forecasting of Global Droughts: Evaluation over USA and Africa

NASA Astrophysics Data System (ADS)

Wood, Eric; Yuan, Xing; Roundy, Joshua; Sheffield, Justin; Pan, Ming

2013-04-01

Extreme hydrologic events in the form of droughts are significant sources of social and economic damage. In the United States according to the National Climatic Data Center, the losses from drought exceed US210 billion during 1980-2011, and account for about 24% of all losses from major weather disasters. Internationally, especially for the developing world, drought has had devastating impacts on local populations through food insecurity and famine. Providing reliable drought forecasts with sufficient early warning will help the governments to move from the management of drought crises to the management of drought risk. After working on drought monitoring and forecasting over the USA for over 10 years, the Princeton land surface hydrology group is now developing a global drought monitoring and forecasting system using a dynamical seasonal climate-hydrologic LSM-model (CHM) approach. Currently there is an active debate on the merits of the CHM-based seasonal hydrologic forecasts as compared to Ensemble Streamflow Prediction (ESP). We use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2) and its previous version CFSv1, to investigate the value of seasonal climate model forecasts by conducting a set of 27-year seasonal hydrologic hindcasts over the USA. Through Bayesian downscaling, climate models have higher squared correlation (R2) and smaller error than ESP for monthly precipitation averaged over major river basins across the USA, and the forecasts conditional on ENSO show further improvements (out to four months) over river basins in the southern USA. All three approaches have plausible predictions of soil moisture drought frequency over central USA out to six months because of strong soil moisture memory, and seasonal climate models provide better results over central and eastern USA. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R2 of drought severity accumulated over USA is higher during winter, and climate models present added value especially at long leads. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the realtime data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for estimating a climatology against which current conditions can be compared. Based on our established experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML), we use the downscaled CFSv2 climate forcings to drive the re-calibrated VIC model and produce 6-month, 20-member ensemble hydrologic forecasts over Africa starting on the 1st of each calendar month during 1982-2007. Our CHM-based seasonal hydrologic forecasts are now being analyzed for its skill in predicting short-term soil moisture droughts over Africa. Besides relying on a single seasonal climate model or a single drought index, preliminary forecast results will be presented using multiple seasonal climate models based on the NOAA-supported National Multi-Model Ensemble (NMME) project, and with multiple drought indices. Results will be presented for the USA NIDIS test beds such as Southeast US and Colorado NIDIS (National Integrated Drought Information System) test beds, and potentially for other regions of the globe.

Population Immunity against Serotype-2 Poliomyelitis Leading up to the Global Withdrawal of the Oral Poliovirus Vaccine: Spatio-temporal Modelling of Surveillance Data.

PubMed

Pons-Salort, Margarita; Molodecky, Natalie A; O'Reilly, Kathleen M; Wadood, Mufti Zubair; Safdar, Rana M; Etsano, Andrew; Vaz, Rui Gama; Jafari, Hamid; Grassly, Nicholas C; Blake, Isobel M

2016-10-01

Global withdrawal of serotype-2 oral poliovirus vaccine (OPV2) took place in April 2016. This marked a milestone in global polio eradication and was a public health intervention of unprecedented scale, affecting 155 countries. Achieving high levels of serotype-2 population immunity before OPV2 withdrawal was critical to avoid subsequent outbreaks of serotype-2 vaccine-derived polioviruses (VDPV2s). In August 2015, we estimated vaccine-induced population immunity against serotype-2 poliomyelitis for 1 January 2004-30 June 2015 and produced forecasts for April 2016 by district in Nigeria and Pakistan. Population immunity was estimated from the vaccination histories of children <36 mo old identified with non-polio acute flaccid paralysis (AFP) reported through polio surveillance, information on immunisation activities with different oral poliovirus vaccine (OPV) formulations, and serotype-specific estimates of the efficacy of these OPVs against poliomyelitis. District immunity estimates were spatio-temporally smoothed using a Bayesian hierarchical framework. Coverage estimates for immunisation activities were also obtained, allowing for heterogeneity within and among districts. Forward projections of immunity, based on these estimates and planned immunisation activities, were produced through to April 2016 using a cohort model. Estimated population immunity was negatively correlated with the probability of VDPV2 poliomyelitis being reported in a district. In Nigeria and Pakistan, declines in immunity during 2008-2009 and 2012-2013, respectively, were associated with outbreaks of VDPV2. Immunity has since improved in both countries as a result of increased use of trivalent OPV, and projections generally indicated sustained or improved immunity in April 2016, such that the majority of districts (99% [95% uncertainty interval 97%-100%] in Nigeria and 84% [95% uncertainty interval 77%-91%] in Pakistan) had >70% population immunity among children <36 mo old. Districts with lower immunity were clustered in northeastern Nigeria and northwestern Pakistan. The accuracy of immunity estimates was limited by the small numbers of non-polio AFP cases in some districts, which was reflected by large uncertainty intervals. Forecasted improvements in immunity for April 2016 were robust to the uncertainty in estimates of baseline immunity (January-June 2015), vaccine coverage, and vaccine efficacy. Immunity against serotype-2 poliomyelitis was forecasted to improve in April 2016 compared to the first half of 2015 in Nigeria and Pakistan. These analyses informed the endorsement of OPV2 withdrawal in April 2016 by the WHO Strategic Advisory Group of Experts on Immunization.

Population Immunity against Serotype-2 Poliomyelitis Leading up to the Global Withdrawal of the Oral Poliovirus Vaccine: Spatio-temporal Modelling of Surveillance Data

PubMed Central

O’Reilly, Kathleen M.; Etsano, Andrew; Vaz, Rui Gama; Jafari, Hamid; Grassly, Nicholas C.; Blake, Isobel M.

2016-01-01

Background Global withdrawal of serotype-2 oral poliovirus vaccine (OPV2) took place in April 2016. This marked a milestone in global polio eradication and was a public health intervention of unprecedented scale, affecting 155 countries. Achieving high levels of serotype-2 population immunity before OPV2 withdrawal was critical to avoid subsequent outbreaks of serotype-2 vaccine-derived polioviruses (VDPV2s). Methods and Findings In August 2015, we estimated vaccine-induced population immunity against serotype-2 poliomyelitis for 1 January 2004–30 June 2015 and produced forecasts for April 2016 by district in Nigeria and Pakistan. Population immunity was estimated from the vaccination histories of children <36 mo old identified with non-polio acute flaccid paralysis (AFP) reported through polio surveillance, information on immunisation activities with different oral poliovirus vaccine (OPV) formulations, and serotype-specific estimates of the efficacy of these OPVs against poliomyelitis. District immunity estimates were spatio-temporally smoothed using a Bayesian hierarchical framework. Coverage estimates for immunisation activities were also obtained, allowing for heterogeneity within and among districts. Forward projections of immunity, based on these estimates and planned immunisation activities, were produced through to April 2016 using a cohort model. Estimated population immunity was negatively correlated with the probability of VDPV2 poliomyelitis being reported in a district. In Nigeria and Pakistan, declines in immunity during 2008–2009 and 2012–2013, respectively, were associated with outbreaks of VDPV2. Immunity has since improved in both countries as a result of increased use of trivalent OPV, and projections generally indicated sustained or improved immunity in April 2016, such that the majority of districts (99% [95% uncertainty interval 97%–100%] in Nigeria and 84% [95% uncertainty interval 77%–91%] in Pakistan) had >70% population immunity among children <36 mo old. Districts with lower immunity were clustered in northeastern Nigeria and northwestern Pakistan. The accuracy of immunity estimates was limited by the small numbers of non-polio AFP cases in some districts, which was reflected by large uncertainty intervals. Forecasted improvements in immunity for April 2016 were robust to the uncertainty in estimates of baseline immunity (January–June 2015), vaccine coverage, and vaccine efficacy. Conclusions Immunity against serotype-2 poliomyelitis was forecasted to improve in April 2016 compared to the first half of 2015 in Nigeria and Pakistan. These analyses informed the endorsement of OPV2 withdrawal in April 2016 by the WHO Strategic Advisory Group of Experts on Immunization. PMID:27701425

Trends in Average Living Children at the Time of Terminal Contraception: A Time Series Analysis Over 27 Years Using ARIMA (p, d, q) Nonseasonal Model.

PubMed

Mumbare, Sachin S; Gosavi, Shriram; Almale, Balaji; Patil, Aruna; Dhakane, Supriya; Kadu, Aniruddha

2014-10-01

India's National Family Welfare Programme is dominated by sterilization, particularly tubectomy. Sterilization, being a terminal method of contraception, decides the final number of children for that couple. Many studies have shown the declining trend in the average number of living children at the time of sterilization over a short period of time. So this study was planned to do time series analysis of the average children at the time of terminal contraception, to do forecasting till 2020 for the same and to compare the rates of change in various subgroups of the population. Data was preprocessed in MS Access 2007 by creating and running SQL queries. After testing stationarity of every series with augmented Dickey-Fuller test, time series analysis and forecasting was done using best-fit Box-Jenkins ARIMA (p, d, q) nonseasonal model. To compare the rates of change of average children in various subgroups, at sterilization, analysis of covariance (ANCOVA) was applied. Forecasting showed that the replacement level of 2.1 total fertility rate (TFR) will be achieved in 2018 for couples opting for sterilization. The same will be achieved in 2020, 2016, 2018, and 2019 for rural area, urban area, Hindu couples, and Buddhist couples, respectively. It will not be achieved till 2020 in Muslim couples. Every stratum of population showed the declining trend. The decline for male children and in rural area was significantly faster than the decline for female children and in urban area, respectively. The decline was not significantly different in Hindu, Muslim, and Buddhist couples.

The Eruption Forecasting Information System (EFIS) database project

NASA Astrophysics Data System (ADS)

Ogburn, Sarah; Harpel, Chris; Pesicek, Jeremy; Wellik, Jay; Pallister, John; Wright, Heather

2016-04-01

The Eruption Forecasting Information System (EFIS) project is a new initiative of the U.S. Geological Survey-USAID Volcano Disaster Assistance Program (VDAP) with the goal of enhancing VDAP's ability to forecast the outcome of volcanic unrest. The EFIS project seeks to: (1) Move away from relying on the collective memory to probability estimation using databases (2) Create databases useful for pattern recognition and for answering common VDAP questions; e.g. how commonly does unrest lead to eruption? how commonly do phreatic eruptions portend magmatic eruptions and what is the range of antecedence times? (3) Create generic probabilistic event trees using global data for different volcano 'types' (4) Create background, volcano-specific, probabilistic event trees for frequently active or particularly hazardous volcanoes in advance of a crisis (5) Quantify and communicate uncertainty in probabilities A major component of the project is the global EFIS relational database, which contains multiple modules designed to aid in the construction of probabilistic event trees and to answer common questions that arise during volcanic crises. The primary module contains chronologies of volcanic unrest, including the timing of phreatic eruptions, column heights, eruptive products, etc. and will be initially populated using chronicles of eruptive activity from Alaskan volcanic eruptions in the GeoDIVA database (Cameron et al. 2013). This database module allows us to query across other global databases such as the WOVOdat database of monitoring data and the Smithsonian Institution's Global Volcanism Program (GVP) database of eruptive histories and volcano information. The EFIS database is in the early stages of development and population; thus, this contribution also serves as a request for feedback from the community.

An Agent-Based Interface to Terrestrial Ecological Forecasting

NASA Technical Reports Server (NTRS)

Golden, Keith; Nemani, Ramakrishna; Pang, Wanlin; Votava, Petr

2005-01-01

The latest generation of NASA Earth Observing System (EOS) satellites has brought a new dimension to continuous monitoring of the living part of the Earth System, the biosphere. EOS data can now provide weekly global measures of vegetation productivity and ocean chlorophyll, and many related biophysical factors such as land cover changes or snowmelt rates. However, the highest economic value would come from forecasting impending conditions of the biosphere, to allow decision makers to mitigate dangers or exploit positive trends. NASA's strategic plan for the Earth Science Enterprise i d e n a s ecological forecasting as a focus for research. Ecological forecasting predicts the effects of changes in the physical, chemical and biological environment on ecosystem activity. Possible applications of such a system include predicting shortfalls or bumper crops of agricultural production, populations of threatened or invasive species or wildfire danger in time to allow improves preparation and logistical efficiency. Petabytes of remote sensing data are now available to help measure, understand and forecast changes in the Earth system, but using these data effectively can be surprisingly hard. The volume and variety of data files and formats are daunting. Simple data management activities, such as locating and transferring files, changing file formats, gridding point data, and scaling and reprojecting gridded data, can consume far more personnel time and resources than the actual data analysis. Some scientists commit to a particular data source or resolution just because using anything different would be more effort that it's worth. Better tools can help, but most of the tools developed to date are little more than shell scripts; they lack the flexibility to meet the diverse needs of users and are difficult to extend to handle changes in available data sources.

An approach to forecasting health expenditures, with application to the U.S. Medicare system.

PubMed

Lee, Ronald; Miller, Timoth

2002-10-01

To quantify uncertainty in forecasts of health expenditures. Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death. A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example. Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita. History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP.

Temporal patterns and forecast of dengue infection in Northeastern Thailand.

PubMed

Silawan, Tassanee; Singhasivanon, Pratap; Kaewkungwal, Jaranit; Nimmanitya, Suchitra; Suwonkerd, Wanapa

2008-01-01

This study aimed to determine temporal patterns and develop a forecasting model for dengue incidence in northeastern Thailand. Reported cases were obtained from the Thailand national surveillance system. The temporal patterns were displayed by plotting monthly rates, the seasonal-trend decomposition procedure based on loess (STL) was performed using R 2.2.1 software, and the trend was assessed using Poisson regression. The forecasting model for dengue incidence was performed in R 2.2.1 and Intercooled Stata 9.2 using the seasonal Autoregressive Integrated Moving Average (ARIMA) model. The model was evaluated by comparing predicted versus actual rates of dengue for 1996 to 2005 and used to forecast monthly rates during January to December 2006. The results reveal that epidemics occurred every two years, with approximately three years per epidemic, and that the next epidemic will take place in 2006 to 2008. It was found that if a month increased, the rate ratio for dengue infection decreased by a factor 0.9919 for overall region and 0.9776 to 0.9984 for individual provinces. The amplitude of the peak, which was evident in June or July, was 11.32 to 88.08 times greater than the rest of the year. The seasonal ARIMA (2, 1, 0) (0, 1, 1)12 model was model with the best fit for regionwide data of total dengue incidence whereas the models with the best fit varied by province. The forecasted regional monthly rates during January to December 2006 should range from 0.27 to 17.89 per 100,000 population. The peak for 2006 should be much higher than the peak for 2005. The highest peaks in 2006 should be in Loei, Buri Ram, Surin, Nakhon Phanom, and Ubon Ratchathani Provinces.

Stakeholders' perceptions of social-ecological systems and the information they use in the management of freshwater resources in Guanacaste, Costa Rica

NASA Astrophysics Data System (ADS)

Wong-Parodi, G.; Babcock, M.; Small, M.; Grossmann, I.

2014-12-01

Climate change is expected to increase the chances of drought, and shift precipitation patterns in seasonally dry places. In some places, the heuristics or "rules of thumb" that stakeholders use may no longer be reliable for the effective management of water resources. This can have dire consequences for social and ecological systems, especially in developing countries. Scientists and policymakers view climate forecasts as one way for improving informed decision-making about freshwater resources. However, successful communication requires that stakeholders understand and are able to use such information. To develop effective communications, it is critical to characterize stakeholders' understanding of social-ecological systems as related to water, the type of information used to inform management decisions, and the perceived value of forecast information. To achieve our objective, we conducted 40 semi-structured interviews with farmers, water managers, hydroelectric utilities, local climate experts, tourism industry representatives, and members of the general public in the semi-arid region of Guanacaste, Costa Rica. People believe that they have enough water at this time however they believe that the region will become much drier in the future, which they attribute to climate change, El Nino/La Nina, and deforestation. With respect to the value of forecast information, we found that the scale of decision-making (e.g., irrigation district versus small farmer) was associated with a stakeholders' level of "technical sophistication" and trust in government. In future work, we will evaluate the prevalence of these beliefs and practices in the larger population in order to identify effective ways to tailor the presentation of forecast information for different audiences. This work provides insight into the development of forecast communications to improve the management of resources in development countries in the face of a changing climate.

Improving the Representation of Snow Crystal Properties Within a Single-Moment Microphysics Scheme

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Dembek, S. R.

2010-01-01

As computational resources continue their expansion, weather forecast models are transitioning to the use of parameterizations that predict the evolution of hydrometeors and their microphysical processes, rather than estimating the bulk effects of clouds and precipitation that occur on a sub-grid scale. These parameterizations are referred to as single-moment, bulk water microphysics schemes, as they predict the total water mass among hydrometeors in a limited number of classes. Although the development of single moment microphysics schemes have often been driven by the need to predict the structure of convective storms, they may also provide value in predicting accumulations of snowfall. Predicting the accumulation of snowfall presents unique challenges to forecasters and microphysics schemes. In cases where surface temperatures are near freezing, accumulated depth often depends upon the snowfall rate and the ability to overcome an initial warm layer. Precipitation efficiency relates to the dominant ice crystal habit, as dendrites and plates have relatively large surface areas for the accretion of cloud water and ice, but are only favored within a narrow range of ice supersaturation and temperature. Forecast models and their parameterizations must accurately represent the characteristics of snow crystal populations, such as their size distribution, bulk density and fall speed. These properties relate to the vertical distribution of ice within simulated clouds, the temperature profile through latent heat release, and the eventual precipitation rate measured at the surface. The NASA Goddard, single-moment microphysics scheme is available to the operational forecast community as an option within the Weather Research and Forecasting (WRF) model. The NASA Goddard scheme predicts the occurrence of up to six classes of water mass: vapor, cloud ice, cloud water, rain, snow and either graupel or hail.

Growth of Errors and Uncertainties in Medium Range Ensemble Forecasts of U.S. East Coast Cool Season Extratropical Cyclones

NASA Astrophysics Data System (ADS)

Zheng, Minghua

Cool-season extratropical cyclones near the U.S. East Coast often have significant impacts on the safety, health, environment and economy of this most densely populated region. Hence it is of vital importance to forecast these high-impact winter storm events as accurately as possible by numerical weather prediction (NWP), including in the medium-range. Ensemble forecasts are appealing to operational forecasters when forecasting such events because they can provide an envelope of likely solutions to serve user communities. However, it is generally accepted that ensemble outputs are not used efficiently in NWS operations mainly due to the lack of simple and quantitative tools to communicate forecast uncertainties and ensemble verification to assess model errors and biases. Ensemble sensitivity analysis (ESA), which employs a linear correlation and regression between a chosen forecast metric and the forecast state vector, can be used to analyze the forecast uncertainty development for both short- and medium-range forecasts. The application of ESA to a high-impact winter storm in December 2010 demonstrated that the sensitivity signals based on different forecast metrics are robust. In particular, the ESA based on the leading two EOF PCs can separate sensitive regions associated with cyclone amplitude and intensity uncertainties, respectively. The sensitivity signals were verified using the leave-one-out cross validation (LOOCV) method based on a multi-model ensemble from CMC, ECMWF, and NCEP. The climatology of ensemble sensitivities for the leading two EOF PCs based on 3-day and 6-day forecasts of historical cyclone cases was presented. It was found that the EOF1 pattern often represents the intensity variations while the EOF2 pattern represents the track variations along west-southwest and east-northeast direction. For PC1, the upper-level trough associated with the East Coast cyclone and its downstream ridge are important to the forecast uncertainty in cyclone strength. The initial differences in forecasting the ridge along the west coast of North America impact the EOF1 pattern most. For PC2, it was shown that the shift of the tri-polar structure is most significantly related to the cyclone track forecasts. The EOF/fuzzy clustering tool was applied to diagnose the scenarios in operational ensemble forecast of East Coast winter storms. It was shown that the clustering method could efficiently separate the forecast scenarios associated with East Coast storms based on the 90-member multi-model ensemble. A scenario-based ensemble verification method has been proposed and applied it to examine the capability of different EPSs in capturing the analysis scenarios for historical East Coast cyclone cases at lead times of 1-9 days. The results suggest that the NCEP model performs better in short-range forecasts in capturing the analysis scenario although it is under-dispersed. The ECMWF ensemble shows the best performance in the medium range. The CMC model is found to show the smallest percentage of members in the analysis group and a relatively high missing rate, suggesting that it is less reliable regarding capturing the analysis scenario when compared with the other two EPSs. A combination of NCEP and CMC models has been found to reduce the missing rate and improve the error-spread skill in medium- to extended-range forecasts. Based on the orthogonal features of the EOF patterns, the model errors for 1-6-day forecasts have been decomposed for the leading two EOF patterns. The results for error decomposition show that the NCEP model tends to better represent both EOF1 and EOF2 patterns by showing less intensity and displacement errors during 1-3 days. The ECMWF model is found to have the smallest errors in both EOF1 and EOF2 patterns during 4-6 days. We have also found that East Coast cyclones in the ECMWF forecast tend to be towards the southwest of the other two models in representing the EOF2 pattern, which is associated with the southwest-northeast shifting of the cyclone. This result suggests that ECMWF model may have a tendency to show a closer-to-shore solution in forecasting East Coast winter storms. The downstream impacts of Rossby wave packets (RWPs) on the predictability of winter storms are investigated to explore the source of ensemble uncertainties. The composited RWPA anomalies show that there are enhanced RWPs propagating across the Pacific in both large-error and large-spread cases over the verification regions. There are also indications that the errors might propagate with a speed comparable with the group velocity of RWPs. Based on the composite results as well as our observations of the operation daily RWPA, a conceptual model of errors/uncertainty development associated with RWPs has been proposed to serve as a practical tool to understand the evolution of forecast errors and uncertainties associated with the coherent RWPs originating from upstream as far as western Pacific. (Abstract shortened by ProQuest.).

Occupational Forecasts for 1998 for Ireland and Their Implications for Educational Qualifications.

ERIC Educational Resources Information Center

Canny, Angela; Hughes, Gerard

The Census of Population provides data on the structure of employment by occupation and industry for Ireland that are supplemented by the Labor Force Survey (LFS), which collects information on employment by occupation and industry. Expected strong growth in the Irish economy from 1993-98 should lead to a significant increase in employment. This…

Nutrition and the biology of human ageing: Proceedings of the ninth nestle international nutrition symposium

USDA-ARS?s Scientific Manuscript database

This 9th Nestle Nutrition Symposium on “Nutrition and the Biology of Human Ageing” is presented at a time of unprecedented demographic change worldwide. The UN population division forecasts that the number of people living over age 65 will rise to almost 1 billion (12% percent of the world’s populat...

Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series

NASA Astrophysics Data System (ADS)

Sugihara, George; May, Robert M.

1990-04-01

An approach is presented for making short-term predictions about the trajectories of chaotic dynamical systems. The method is applied to data on measles, chickenpox, and marine phytoplankton populations, to show how apparent noise associated with deterministic chaos can be distinguished from sampling error and other sources of externally induced environmental noise.

Base Redevelopment Planning for BRAC Sites

DTIC Science & Technology

2006-05-01

or the private sector, also may supplement core staff responsibilities. Pro - fessional consultants may provide legal, planning, real estate...Opportunities, and Threats ( SWOT ) Analysis: An evaluation of a community’s economic, social, and physical environmental strengths, weaknesses, opportunities...in this area compare to those elsewhere? Forecasted changes in employment and population trends can be translated into pro - jected demand for housing

Isolating weather effects from seasonal activity patterns of a temperate North American Colubrid

Treesearch

Andrew D. George; Frank R. III Thompson; John Faaborg

2015-01-01

Forecasting the effects of climate change on threatened ecosystems and species will require an understanding of how weather influences processes that drive population dynamics. We have evaluated weather effects on activity patterns of western ratsnakes, a widespread predator of birds and small mammals in eastern North America. From 2010-2013 we radio-tracked 53...

Neither Feast nor Famine: Summary of the Second Twenty Year Forecast Project.

ERIC Educational Resources Information Center

Enzer, Selwyn; And Others

1977-01-01

The key question to ask in determining whether a solution will be found to the world food problem is whether people will learn to effectively manage the food/population balance. Predictions concerning the world food situation should be made on the basis of these factors: (1) possible future changes involving technological development, political…

Spatial estimation of the density and carbon content of host populations for Phytophthora ramorum in California and Oregon

Treesearch

Sanjay Lamsal; Richard C. Cobb; J. Hall Cushman; Qingmin Meng; David M. Rizzo; Ross K. Meentemeyer.

2011-01-01

Outbreak of the emerging infectious disease sudden oak death continues to threaten California and Oregon forests following introduction of the exotic plant pathogen Phytophthora ramorum. Identifying areas at risk and forecasting changes in forest carbon following disease outbreak requires an understanding of the geographical distribution of host...

A methodology for assessing annual risk of southern pine beetle outbreaks across the southern region using pheromone traps

Treesearch

Ronald F Billings; William W. Upton

2010-01-01

An operational system to forecast infestation trends (increasing, static, declining) and relative population levels (high, moderate, low) of the southern pine beetle (SPB), Dendroctonus frontalis, has been implemented in the Southern and Eastern United States. Numbers of dispersing SPB and those of a major predator (the clerid beetle, ...

A bayesian cross-validation approach to evaluate genetic baselines and forecast the necessary number of informative single nucleotide polymorphisms

USDA-ARS?s Scientific Manuscript database

Mixed stock analysis (MSA) is a powerful tool used in the management and conservation of numerous species. Its function is to estimate the sources of contributions in a mixture of populations of a species, as well as to estimate the probabilities that individuals originated at a source. Considerable...

Integration of Airborne Aerosol Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Sprigg, William A.; Huete, Alfredo; Pejanovic, Goran; Nickovic,Slobodan; Ponce-Campos, Guillermo; Krapfl, Heide; Budge, Amy; Zelicoff, Alan; VandeWater, Peter K.;

Market-based demand forecasting promotes informed strategic financial planning.

PubMed

Beech, A J

2001-11-01

Market-based demand forecasting is a method of estimating future demand for a healthcare organization's services by using a broad range of data that describe the nature of demand within the organization's service area. Such data include the primary and secondary service areas, the service-area populations by various demographic groupings, discharge utilization rates, market size, and market share by service line and organizationwide. Based on observable market dynamics, strategic planners can make a variety of explicit assumptions about future trends regarding these data to develop scenarios describing potential future demand. Financial planners then can evaluate each scenario to determine its potential effect on selected financial and operational measures, such as operating margin, days cash on hand, and debt-service coverage, and develop a strategic financial plan that covers a range of contingencies.

Environmentally Related Diseases and the Possibility of Valuation of Their Social Costs

PubMed Central

Hajok, Ilona; Marchwińska, Ewa; Dziubanek, Grzegorz; Kuraszewska, Bernadeta; Piekut, Agata

2014-01-01

The risks of the morbidity of the asbestos-related lung cancer was estimated in the general population of Poles as the result of increased exposure to asbestos fibers during the removal of asbestos-cement products and the possibility of the valuation of the social costs related to this risk. The prediction of the new incidences was made using linear regression model. The forecast shows that to the end of 2030 about 3,500 new cases of lung cancer can be expected as a result of occupational exposure to asbestos in the past which makes together with paraoccupational exposure about 14.000 new cases. The forecast shows the increasing number of asbestos-related lung cancer in Poland and indicates the priority areas where preventive action should be implemented. PMID:25374934

Identifying open and closed system behaviors at Tungurahua volcano (Ecuador) using SO2 and seismo-acoustic measurements

NASA Astrophysics Data System (ADS)

Hidalgo, Silvana; Battaglia, Jean; Bernard, Benjamin; Steele, Alexander; Arellano, Santiago; Galle, Bo

2014-05-01

Tungurahua is one of the most active volcanoes in Ecuador. It is located in Central Ecuador, 160 km South of Quito and 8 km South of the touristic town of Baños. Tungurahua had one eruption every century since 1500, with an activity characterized by ash fallouts and pyroclastic and lava flows. The current eruptive period of Tungurahua began in 1999 with multiple episodes of explosive activity that have threatened the local population. The monitoring network is constituted by 5 short period and 5 broadband seismic stations, 4 DOAS permanent instruments, 4 tiltmeters, 2 permanent high resolution GPS, 4 digital cameras and 10 acoustic flow monitors. The correct interpretation of the different data acquired by this network allows a better understanding of the eruptive behavior of Tungurahua in order to provide early warning to the local population. Tungurahua changed its behavior from a continuously erupting volcano, as it was until 2008, to a sporadically erupting one, showing clear quiescence phases lasting from 40 to 184 days, and intense activity phases lasting from 15 to 70 days. Activity phases are characterized by Strombolian and Vulcanian eruptive styles, producing ash fallouts and in a few occasions pyroclastic flows. In terms of hazard to the local population, one of the goals of monitoring Tungurahura is to forecast the onset and evolution of eruptive phases. In particular the occurrence of large Vulcanian explosions which occur when the conduit is closed is a major issue. Since 2010 we focused our study on the relation between SO2 gas emissions, the seismic and acoustic energies of explosions and the tremor amplitudes. The first observation of comparing these different datasets is that the correlation between seismic and SO2 degassing is not straightforward, and actually the relation reflects the conditions at the vent: open or closed. The onset of eruptive phases in open conduit conditions can be identified which leads to an effective eruption forecasting. An example of this behavior is the eruptive phase between December 2009 and March 2010 when SO2 measurements increased 4 days before the amplitude of tremor and 9 days before the occurrence of the first explosions. Conversely, if the vent is closed at the beginning of a phase and no evident seismic precursors are observed forecasting is hardly possible. During an ongoing eruptive phase, the relation between these parameters allows to identify periods when the conduit is totally open as degassing may occur almost without generating any seismicity. Therefore the forecasting of escalating open conduit activity or a partial closing of the system is possible. Such a case was observed and forecasted on December 2011. In this work, we present observational evidence of these mechanisms which are used to identify possible patterns of evolution of the activity, contributing to a more effective volcanic hazard assessment.

Separating direct and indirect effects of global change: a population dynamic modeling approach using readily available field data.

PubMed

Farrer, Emily C; Ashton, Isabel W; Knape, Jonas; Suding, Katharine N

2014-04-01

Two sources of complexity make predicting plant community response to global change particularly challenging. First, realistic global change scenarios involve multiple drivers of environmental change that can interact with one another to produce non-additive effects. Second, in addition to these direct effects, global change drivers can indirectly affect plants by modifying species interactions. In order to tackle both of these challenges, we propose a novel population modeling approach, requiring only measurements of abundance and climate over time. To demonstrate the applicability of this approach, we model population dynamics of eight abundant plant species in a multifactorial global change experiment in alpine tundra where we manipulated nitrogen, precipitation, and temperature over 7 years. We test whether indirect and interactive effects are important to population dynamics and whether explicitly incorporating species interactions can change predictions when models are forecast under future climate change scenarios. For three of the eight species, population dynamics were best explained by direct effect models, for one species neither direct nor indirect effects were important, and for the other four species indirect effects mattered. Overall, global change had negative effects on species population growth, although species responded to different global change drivers, and single-factor effects were slightly more common than interactive direct effects. When the fitted population dynamic models were extrapolated under changing climatic conditions to the end of the century, forecasts of community dynamics and diversity loss were largely similar using direct effect models that do not explicitly incorporate species interactions or best-fit models; however, inclusion of species interactions was important in refining the predictions for two of the species. The modeling approach proposed here is a powerful way of analyzing readily available datasets which should be added to our toolbox to tease apart complex drivers of global change. © 2013 John Wiley & Sons Ltd.

Forecasting Financial Resources for Future Traumatic Spinal Cord Injury Care Using Simulation Modeling

PubMed Central

Ahn, Henry; Lewis, Rachel; Santos, Argelio; Cheng, Christiana L.; Dvorak, Marcel F.; Singh, Anoushka; Linassi, A. Gary; Christie, Sean; Goytan, Michael; Atkins, Derek

2017-01-01

Abstract Survivors of traumatic spinal cord injury (tSCI) have intense healthcare needs during acute and rehabilitation care and often through the rest of life. To prepare for a growing and aging population, simulation modeling was used to forecast the change in healthcare financial resources and long-term patient outcomes between 2012 and 2032. The model was developed with data from acute and rehabilitation care facilities across Canada participating in the Access to Care and Timing project. Future population and tSCI incidence for 2012 and 2032 were predicted with data from Statistics Canada and the Canadian Institute for Health Information. The projected tSCI incidence for 2012 was validated with actual data from the Rick Hansen SCI Registry of the participating facilities. Using a medium growth scenario, in 2032, the projected median age of persons with tSCI is 57 and persons 61 and older will account for 46% of injuries. Admissions to acute and rehabilitation facilities in 2032 were projected to increase by 31% and 25%, respectively. Because of the demographic shift to an older population, an increase in total population life expectancy with tSCI of 13% was observed despite a 22% increase in total life years lost to tSCI between 2012 and 2032. Care cost increased 54%, and rest of life cost increased 37% in 2032, translating to an additional CAD $16.4 million. With the demographics and management of tSCI changing with an aging population, accurate projections for the increased demand on resources will be critical for decision makers when planning the delivery of healthcare after tSCI. PMID:28594315

Development of an Experimental African Drought Monitoring and Seasonal Forecasting System: A First Step towards a Global Drought Information System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Chaney, N.; Sheffield, J.; Yuan, X.

2012-12-01

Extreme hydrologic events in the form of droughts are a significant source of social and economic damage. Internationally, organizations such as UNESCO, the Group on Earth Observations (GEO), and the World Climate Research Programme (WCRP) have recognized the need for drought monitoring, especially for the developing world where drought has had devastating impacts on local populations through food insecurity and famine. Having the capacity to monitor droughts in real-time, and to provide drought forecasts with sufficient warning will help developing countries and international programs move from the management of drought crises to the management of drought risk. While observation-based assessments, such as those produced by the US Drought Monitor, are effective for monitoring in countries with extensive observation networks (of precipitation in particular), their utility is lessened in areas (e.g., Africa) where observing networks are sparse. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the real-time data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for the construction of a climatology against which current conditions can be compared. In this presentation we discuss the development of our multi-lingual experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML). At the request of UNESCO, the ADM system has been installed at AGRHYMET, a regional climate and agricultural center in Niamey, Niger and at the ICPAC climate center in Nairobi, Kenya. The ADM system leverages off our U.S. drought monitoring and forecasting system (http://hydrology.princeton.edu/forecasting) that uses the NLDAS data to force the VIC land surface model (LSM) at 1/8th degree spatial resolution for the estimation of our soil moisture drought index (Sheffield et al., 2004). For the seasonal forecast of drought, CFSv2 climate forecasts are bias corrected, downscaled and used as inputs to the VIC LSM as well as forecasts based on ESP and CPC official seasonal outlook. For Africa, data from a combination of remote sensing (TMPA-based precipitation, land cover characteristics) and GFS analysis fields (temperature and wind) are used to monitor drought using our soil moisture drought index as well as 1, 3 and and 6-month SPI. River discharge is also estimated at over 900 locations. Seasonal forecasts have been developed using CFSv2 climate forecasts following the approaches used over CONUS. We will discuss the performance of the system to evaluate the depiction of drought over various scales, from regional to the African continent, and over a number of years to capture multiple drought events. Furthermore, the hindcasts from the seasonal drought forecast system are analyzed to assess the ability of seasonal climate models to detect drought on-set and its recovery. Finally, we will discuss whether our ADM provides a pathway to a Global Drought Information System, a goal of the WCRP Drought Task Force.

A System Dynamics Modeling of Water Supply and Demand in Las Vegas Valley

NASA Astrophysics Data System (ADS)

Parajuli, R.; Kalra, A.; Mastino, L.; Velotta, M.; Ahmad, S.

2017-12-01

The rise in population and change in climate have posed the uncertainties in the balance between supply and demand of water. The current study deals with the water management issues in Las Vegas Valley (LVV) using Stella, a system dynamics modeling software, to model the feedback based relationship between supply and demand parameters. Population parameters were obtained from Center for Business and Economic Research while historical water demand and conservation practices were modeled as per the information provided by local authorities. The water surface elevation of Lake Mead, which is the prime source of water supply to the region, was modeled as the supply side whereas the water demand in LVV was modeled as the demand side. The study was done from the period of 1989 to 2049 with 1989 to 2012 as the historical one and the period from 2013 to 2049 as the future period. This study utilizes Coupled Model Intercomparison Project data sets (2013-2049) (CMIP3&5) to model different future climatic scenarios. The model simulates the past dynamics of supply and demand, and then forecasts the future water budget for the forecasted future population and future climatic conditions. The results can be utilized by the water authorities in understanding the future water status and hence plan suitable conservation policies to allocate future water budget and achieve sustainable water management.

Applications of monsoon research: Opportunities to inform decisionmaking and reduce regional vulnerability

NASA Astrophysics Data System (ADS)

Ray, A. J.; Garfin, G. M.; Wilder, M.; Lenart, M.; Vásquez-León, M.; Comrie, A. C.

2007-05-01

This presentation will describe ongoing efforts to understand interactions between the North American Monsoon and society, in order to develop applications for monsoon research in a highly complex, multicultural and binational region. The North American Monsoon is an annual precipitation regime that begins in early June in Mexico and progresses northward to the southwestern United States. The region includes stakeholders in large urban complexes, productive agricultural areas, and sparsely populated arid and semi-arid ecosystems. The political, cultural, and socioeconomic divisions between the U.S. and Mexico create a broad range of sensitivities to climate variability as well as capacities to use forecasts and other information to cope with climate. We will highlight methodologies to link climate science with society and analyze opportunities for monsoon science to benefit society in four sectors: natural hazards management, agriculture, public health, and water management. We present a synthesized list of stakeholder needs and a calendar of decisions to help scientists link user needs to potential forecasts and products. To ensure usability of forecasts and other research products, we recommend iterative scientist-stakeholder interactions, through integrated assessments. These knowledge- exchange interactions can improve the capacity for stakeholders to use forecasts thoughtfully and inform the development of research, and for the research community to obtain feedback on climate-related products and receive insights to guide research direction. We expect that integrated assessments can capitalize on the opportunities for monsoon science to inform decisionmaking, in the best instances, reduce regional climate vulnerabilities and enhance regional sustainability

Improvements on the relationship between plume height and mass eruption rate: Implications for volcanic ash cloud forecasting

NASA Astrophysics Data System (ADS)

Webley, P. W.; Dehn, J.; Mastin, L. G.; Steensen, T. S.

2011-12-01

Volcanic ash plumes and the dispersing clouds into the atmosphere are a hazard for local populations as well as for the aviation industry. Volcanic ash transport and dispersion (VATD) models, used to forecast the movement of these hazardous ash emissions, require eruption source parameters (ESP) such as plume height, eruption rate and duration. To estimate mass eruption rate, empirical relationships with observed plume height have been applied. Theoretical relationships defined by Morton et al. (1956) and Wilson et al. (1976) use default values for the environmental lapse rate (ELR), thermal efficiency, density of ash, specific heat capacity, initial temperature of the erupted material and final temperature of the material. Each volcano, based on its magma type, has a different density, specific heat capacity and initial eruptive temperature compared to these default parameters, and local atmospheric conditions can produce a very different ELR. Our research shows that a relationship between plume height and mass eruption rate can be defined for each eruptive event for each volcano. Additionally, using the one-dimensional modeling program, Plumeria, our analysis assesses the importance of factors such as vent diameter and eruption velocity on the relationship between the eruption rate and measured plume height. Coupling such a tool with a VATD model should improve pre-eruptive forecasts of ash emissions downwind and lead to improvements in ESP data that VATD models use for operational volcanic ash cloud forecasting.

An econometric simulation model of income and electricity demand in Alaska's Railbelt, 1982-2022

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maddigan, R.J.; Hill, L.J.; Hamblin, D.M.

1987-01-01

This report describes the specification of-and forecasts derived from-the Alaska Railbelt Electricity Load, Macroeconomic (ARELM) model. ARELM was developed as an independent, modeling tool for the evaluation of the need for power from the Susitna Hydroelectric Project which has been proposed by the Alaska Power Authority. ARELM is an econometric simulation model consisting of 61 equations - 46 behavioral equations and 15 identities. The system includes two components: (1) ARELM-MACRO which is a system of equations that simulates the performance of both the total Alaskan and Railbelt macroeconomies and (2) ARELM-LOAD which projects electricity-related activity in the Alaskan Railbelt region.more » The modeling system is block recursive in the sense that forecasts of population, personal income, and employment in the Railbelt derived from ARELM-MACRO are used as explanatory variables in ARELM-LOAD to simulate electricity demand, the real average price of electricity, and the number of customers in the Railbelt. Three scenarios based on assumptions about the future price of crude oil are simulated and documented in the report. The simulations, which do not include the cost-of-power impacts of Susitna-based generation, show that the growth rate in Railbelt electricity load is between 2.5 and 2.7% over the 1982 to 2022 forecast period. The forecasting results are consistent with other projections of load growth in the region using different modeling approaches.« less

Multi-Scale Enviro-HIRLAM Forecasting of Weather and Atmospheric Composition over China and its Megacities

NASA Astrophysics Data System (ADS)

Mahura, Alexander; Amstrup, Bjarne; Nuterman, Roman; Yang, Xiaohua; Baklanov, Alexander

2017-04-01

Air pollution is a serious problem in different regions of China and its continuously growing megacities. Information on air quality, and especially, in urbanized areas is important for decision making, emergency response and population. In particular, the metropolitan areas of Shanghai, Beijing, and Pearl River Delta are well known as main regions having serious air pollution problems. The on-line integrated meteorology-chemistry-aerosols Enviro-HIRLAM (Environment - HIgh Resolution Limited Area Model) model adapted for China and selected megacities is applied for forecasting of weather and atmospheric composition (with focus on aerosols). The model system is running in downscaling chain from regional to urban scales at subsequent horizontal resolutions of 15-5-2.5 km. The model setup includes also the urban Building Effects Parameterization module, describing different types of urban districts (industrial commercial, city center, high density and residential) with its own morphological and aerodynamical characteristics. The effects of urbanization are important for atmospheric transport, dispersion, deposition, and chemical transformations, in addition to better quality emission inventories for China and selected urban areas. The Enviro-HIRLAM system provides meteorology and air quality forecasts at regional-subregional-urban scales (China - East China - selected megacities). In particular, such forecasting is important for metropolitan areas, where formation and development of meteorological and chemical/aerosol patterns are especially complex. It also provides information for evaluation impact on selected megacities of China as well as for investigation relationship between air pollution and meteorology.

Atmosphere-Wave-Ocean Coupling from Regional to Global Earth System Models for High-Impact Extreme Weather Prediction

NASA Astrophysics Data System (ADS)

Chen, S. S.; Curcic, M.

2017-12-01

The need for acurrate and integrated impact forecasts of extreme wind, rain, waves, and storm surge is growing as coastal population and built environment expand worldwide. A key limiting factor in forecasting impacts of extreme weather events associated with tropical cycle and winter storms is fully coupled atmosphere-wave-ocean model interface with explicit momentum and energy exchange. It is not only critical for accurate prediction of storm intensity, but also provides coherent wind, rian, ocean waves and currents forecasts for forcing for storm surge. The Unified Wave INterface (UWIN) has been developed for coupling of the atmosphere-wave-ocean models. UWIN couples the atmosphere, wave, and ocean models using the Earth System Modeling Framework (ESMF). It is a physically based and computationally efficient coupling sytem that is flexible to use in a multi-model system and portable for transition to the next generation global Earth system prediction mdoels. This standardized coupling framework allows researchers to develop and test air-sea coupling parameterizations and coupled data assimilation, and to better facilitate research-to-operation activities. It has been used and extensively tested and verified in regional coupled model forecasts of tropical cycles and winter storms (Chen and Curcic 2016, Curcic et al. 2016, and Judt et al. 2016). We will present 1) an overview of UWIN and its applications in fully coupled atmosphere-wave-ocean model predictions of hurricanes and coastal winter storms, and 2) implenmentation of UWIN in the NASA GMAO GEOS-5.

Developing an online tool for identifying at-risk populations to wildfire smoke hazards.

PubMed

Vaidyanathan, Ambarish; Yip, Fuyuen; Garbe, Paul

2018-04-01

Wildfire episodes pose a significant public health threat in the United States. Adverse health impacts associated with wildfires occur near the burn area as well as in places far downwind due to wildfire smoke exposures. Health effects associated with exposure to particulate matter arising from wildfires can range from mild eye and respiratory tract irritation to more serious outcomes such as asthma exacerbation, bronchitis, and decreased lung function. Real-time operational forecasts of wildfire smoke concentrations are available but they are not readily integrated with information on vulnerable populations necessary to identify at-risk communities during wildfire smoke episodes. Efforts are currently underway at the Centers for Disease Control and Prevention (CDC) to develop an online tool that utilizes short-term predictions and forecasts of smoke concentrations and integrates them with measures of population-level vulnerability for identifying at-risk populations to wildfire smoke hazards. The tool will be operationalized on a national scale, seeking input and assistance from several academic, federal, state, local, Tribal, and Territorial partners. The final product will then be incorporated into CDC's National Environmental Public Health Tracking Network (http://ephtracking.cdc.gov), providing users with access to a suite of mapping and display functionalities. A real-time vulnerability assessment tool incorporating standardized health and exposure datasets, and prevention guidelines related to wildfire smoke hazards is currently unavailable for public health practitioners and emergency responders. This tool could strengthen existing situational awareness competencies, and expedite future response and recovery efforts during wildfire episodes. Published by Elsevier B.V.

Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring.

PubMed

Netterberg, Ida; Nielsen, Elisabet I; Friberg, Lena E; Karlsson, Mats O

2017-08-01

To investigate whether a more frequent monitoring of the absolute neutrophil counts (ANC) during myelosuppressive chemotherapy, together with model-based predictions, can improve therapy management, compared to the limited clinical monitoring typically applied today. Daily ANC in chemotherapy-treated cancer patients were simulated from a previously published population model describing docetaxel-induced myelosuppression. The simulated values were used to generate predictions of the individual ANC time-courses, given the myelosuppression model. The accuracy of the predicted ANC was evaluated under a range of conditions with reduced amount of ANC measurements. The predictions were most accurate when more data were available for generating the predictions and when making short forecasts. The inaccuracy of ANC predictions was highest around nadir, although a high sensitivity (≥90%) was demonstrated to forecast Grade 4 neutropenia before it occurred. The time for a patient to recover to baseline could be well forecasted 6 days (±1 day) before the typical value occurred on day 17. Daily monitoring of the ANC, together with model-based predictions, could improve anticancer drug treatment by identifying patients at risk for severe neutropenia and predicting when the next cycle could be initiated.

A signal processing based analysis and prediction of seizure onset in patients with epilepsy

PubMed Central

Namazi, Hamidreza; Kulish, Vladimir V.

2016-01-01

One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More than 30% of people with epilepsy cannot control seizure. Epileptic seizure prediction, refers to forecasting the occurrence of epileptic seizures, is one of the most important but challenging problems in biomedical sciences, across the world. In this research we propose a new methodology which is based on studying the EEG signals using two measures, the Hurst exponent and fractal dimension. In order to validate the proposed method, it is applied to epileptic EEG signals of patients by computing the Hurst exponent and fractal dimension, and then the results are validated versus the reference data. The results of these analyses show that we are able to forecast the onset of a seizure on average of 25.76 seconds before the time of occurrence. PMID:26586477

A latent process model for forecasting multiple time series in environmental public health surveillance.

PubMed

Morrison, Kathryn T; Shaddick, Gavin; Henderson, Sarah B; Buckeridge, David L

2016-08-15

This paper outlines a latent process model for forecasting multiple health outcomes arising from a common environmental exposure. Traditionally, surveillance models in environmental health do not link health outcome measures, such as morbidity or mortality counts, to measures of exposure, such as air pollution. Moreover, different measures of health outcomes are treated as independent, while it is known that they are correlated with one another over time as they arise in part from a common underlying exposure. We propose modelling an environmental exposure as a latent process, and we describe the implementation of such a model within a hierarchical Bayesian framework and its efficient computation using integrated nested Laplace approximations. Through a simulation study, we compare distinct univariate models for each health outcome with a bivariate approach. The bivariate model outperforms the univariate models in bias and coverage of parameter estimation, in forecast accuracy and in computational efficiency. The methods are illustrated with a case study using healthcare utilization and air pollution data from British Columbia, Canada, 2003-2011, where seasonal wildfires produce high levels of air pollution, significantly impacting population health. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A long-term forecast analysis on worldwide land uses.

PubMed

Zhang, Wenjun; Qi, Yanhong; Zhang, Zhiguo

2006-08-01

More and more lands worldwide are being cultivated for food production while forests are disappearing at an unprecedented rate. This paper aims to make a long-term forecast on land uses worldwide and provide the public, researchers, and government officials with a clear profile for land uses in the future. Data of land uses since 1961 were used to fit historical trajectories and make the forecast. The results show that trajectories of land areas can be well fitted with univariate linear regressions. The forecasts of land uses during the coming 25 years were given in detail. Areas of agricultural land, arable land, and permanent pasture land worldwide would increase by 6.6%, 7.2%, and 6.3% respectively in the year 2030 as compared to the current areas. Permanent crops land area all over the world is forecasted to increase 0.64% by 2030. By the year 2030 the areas of forests and woodland, nonarable and nonpermanent land worldwide would decrease by 2.4% and 0.9% against the current areas. All other land area in the world would dramatically decline by 6.4% by the year 2030. Overall the land area related to agriculture would tend to decrease in developed countries, industrialized countries, Europe, and North and Central America. The agriculture related land area would considerably increase in developing countries, least developed countries, low-income countries, Asia, Africa, South America, etc. Developing countries hold larger total land area than developed countries. Dramatic and continuous growth in agricultural land area of developing countries would largely contribute to the expected growth of world agricultural land area in the coming years. Population explosion, food shortage and poverty in the world, especially in developing countries, together caused the excessive cultivation of land for agricultural uses in the past years. Increasing agricultural land area exacerbates the climate changes and degradation of environment. How to limit the growth of human population is a key problem for reducing agricultural land expansion. Development and use of high-yielding and high-quality crop and animal varieties, diversification of human food sources, and technical and financial assistance to developing countries from developed countries, should also be implemented and strengthened in the future in order to slow down or even reverse the increase trend of agricultural land area. Sustainable agriculture is the effective way to stabilize the agricultural land area without food shortage. Through various techniques and measures, sustainable agriculture may meet the food production goals with minimum environmental risk. Public awareness and interest in sustainable agriculture will help realize and ease the increasing stress from agricultural land expansion.

Potential effects of climate change on freshwater ecosystems of the New England/Mid-Atlantic Region

Treesearch

Marianne V. Moore; Michael L. Pace; John R. Mather; [and others; [Editor’s note: Patricia A. Flebbe is the SRS co-author for this publication.

1997-01-01

Numerous freshwater ecosystems, dense concentrations of humans along the eastern seaboard, extensive forests, and a history of intensive land use distinguish the New England/Mid-Atlantic Region. Human population densities are forecast to increase in portions of the region at the same time that climate is expected to be changing. Consequently, the effects of humans and...

Requirements for new housing in Hawaii, 1965-70 ... a forecast

Treesearch

John D. Zinnikas; Boone Sidney

1967-01-01

An estimated 44,300 new housing units will be required in Hawaii during the period 1965-70. Single family houses are expected to comprise 20,000 to 26,000 of these units. Housing demand is assumed to be related to population growth, age of existing housing units, and disposable income of potential home buyers. The factors affecting house construction are described....

"Now, Year Ones, This Is Your Life!" Preparing the Present Generation of Students for a World of Shrinking Distances.

ERIC Educational Resources Information Center

Beare, Hedley

2001-01-01

Forecasts for the future are made against the backdrop of population growth, environmental change, information technology, and globalization. Schools and teachers as we know them will change radically, perhaps become obsolete, as computers and the Internet enable access to information from anywhere, any time. Learning will become a life-long,…

From Camouflage to Classroom: Designing a Transition Curriculum for New Student Veterans

ERIC Educational Resources Information Center

Osborne, Nicholas J.

2016-01-01

The landscape of higher education necessitates that strategies be in place to meet the needs of an ever changing student population. Since 2009, the Post-9/11 GI Bill has spurred an increased enrollment of student veterans that is forecasted to rise. Students who are veterans have unique experiences related to their service, age, and work-life…

Use of Wildfire Smoke Forcasting Model to Mitigate Burden on a Population's Health and Wellbeing Presentation for International Society of Environmental Epidemiology meeting

EPA Science Inventory

Use of Wildfire Smoke Forecasting Model to Mitigate Burden on a Population’s Health and Wellbeing Ana G. Rappold, Neal Fann, Wayne E. Cascio, Robert B. Devlin, David Diaz-Sanchez Background Wildfires are a major source of fine particular matter and other air pollutants as...

A supply model for nurse workforce projection in Malaysia.

PubMed

Abas, Zuraida Abal; Ramli, Mohamad Raziff; Desa, Mohamad Ishak; Saleh, Nordin; Hanafiah, Ainul Nadziha; Aziz, Nuraini; Abidin, Zaheera Zainal; Shibghatullah, Abdul Samad; Rahman, Ahmad Fadzli Nizam Abdul; Musa, Haslinda

2017-08-18

The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world

PubMed Central

Webster, Peter J.; Jian, Jun

2011-01-01

The uncertainty associated with predicting extreme weather events has serious implications for the developing world, owing to the greater societal vulnerability to such events. Continual exposure to unanticipated extreme events is a contributing factor for the descent into perpetual and structural rural poverty. We provide two examples of how probabilistic environmental prediction of extreme weather events can support dynamic adaptation. In the current climate era, we describe how short-term flood forecasts have been developed and implemented in Bangladesh. Forecasts of impending floods with horizons of 10 days are used to change agricultural practices and planning, store food and household items and evacuate those in peril. For the first time in Bangladesh, floods were anticipated in 2007 and 2008, with broad actions taking place in advance of the floods, grossing agricultural and household savings measured in units of annual income. We argue that probabilistic environmental forecasts disseminated to an informed user community can reduce poverty caused by exposure to unanticipated extreme events. Second, it is also realized that not all decisions in the future can be made at the village level and that grand plans for water resource management require extensive planning and funding. Based on imperfect models and scenarios of economic and population growth, we further suggest that flood frequency and intensity will increase in the Ganges, Brahmaputra and Yangtze catchments as greenhouse-gas concentrations increase. However, irrespective of the climate-change scenario chosen, the availability of fresh water in the latter half of the twenty-first century seems to be dominated by population increases that far outweigh climate-change effects. Paradoxically, fresh water availability may become more critical if there is no climate change. PMID:22042897

Forecasting the Value of Podiatric Medical Care in Newly Insured Diabetic Patients During Implementation of the Affordable Care Act in California.

PubMed

Labovitz, Jonathan M; Kominski, Gerald F

2016-05-01

Because value-based care is critical to the Affordable Care Act success, we forecasted inpatient costs and the potential impact of podiatric medical care on savings in the diabetic population through improved care quality and decreased resource use during implementation of the health reform initiatives in California. We forecasted enrollment of diabetic adults into Medicaid and subsidized health benefit exchange programs using the California Simulation of Insurance Markets (CalSIM) base model. Amputations and admissions per 1,000 diabetic patients and inpatient costs were based on the California Office of Statewide Health Planning and Development 2009-2011 inpatient discharge files. We evaluated cost in three categories: uncomplicated admissions, amputations during admissions, and discharges to a skilled nursing facility. Total costs and projected savings were calculated by applying the metrics and cost to the projected enrollment. Diabetic patients accounted for 6.6% of those newly eligible for Medicaid or health benefit exchange subsidies, with a 60.8% take-up rate. We project costs to be $24.2 million in the diabetic take-up population from 2014 to 2019. Inpatient costs were 94.3% higher when amputations occurred during the admission and 46.7% higher when discharged to a skilled nursing facility. Meanwhile, 61.0% of costs were attributed to uncomplicated admissions. Podiatric medical services saved 4.1% with a 10% reduction in admissions and amputations and an additional 1% for every 10% improvement in access to podiatric medical care. When implementing the Affordable Care Act, inclusion of podiatric medical services on multidisciplinary teams and in chronic-care models featuring prevention helps shift care to ambulatory settings to realize the greatest cost savings.

Modelling Future Cardiovascular Disease Mortality in the United States: National Trends and Racial and Ethnic Disparities

PubMed Central

Pearson-Stuttard, Jonathan; Guzman-Castillo, Maria; Penalvo, Jose L.; Rehm, Colin D.; Afshin, Ashkan; Danaei, Goodarz; Kypridemos, Chris; Gaziano, Tom; Mozaffarian, Dariush; Capewell, Simon; O’Flaherty, Martin

2016-01-01

Background Accurate forecasting of cardiovascular disease (CVD) mortality is crucial to guide policy and programming efforts. Prior forecasts have often not incorporated past trends in rates of reduction in CVD mortality. This creates uncertainties about future trends in CVD mortality and disparities. Methods and Results To forecast US CVD mortality and disparities to 2030, we developed a hierarchical Bayesian model to determine and incorporate prior age, period and cohort (APC) effects from 1979–2012, stratified by age, gender and race; which we combined with expected demographic shifts to 2030. Data sources included the National Vital Statistics System, SEER single year population estimates, and US Bureau of Statistics 2012 National Population projections. We projected coronary disease and stroke deaths to 2030, first based on constant APC effects at 2012 values, as most commonly done (conventional); and then using more rigorous projections incorporating expected trends in APC effects (trend-based). We primarily evaluated absolute mortality. The conventional model projected total coronary and stroke deaths by 2030 to increase by approximately 18% (67,000 additional coronary deaths/year) and 50% (64,000 additional stroke deaths/year). Conversely, the trend-based model projected that coronary mortality would fall by 2030 by approximately 27% (79,000 fewer deaths/year); and stroke mortality would remain unchanged (200 fewer deaths/year). Health disparities will be improved in stroke deaths, but not coronary deaths. Conclusions After accounting for prior mortality trends and expected demographic shifts, total US coronary deaths are expected to decline, while stroke mortality will remain relatively constant. Health disparities in stroke, but not coronary, deaths will be improved but not eliminated. These APC approaches offer more plausible predictions than conventional estimates. PMID:26846769

Sensitivity to volcanic field boundary

NASA Astrophysics Data System (ADS)

Runge, Melody; Bebbington, Mark; Cronin, Shane; Lindsay, Jan; Rashad Moufti, Mohammed

2016-04-01

Volcanic hazard analyses are desirable where there is potential for future volcanic activity to affect a proximal population. This is frequently the case for volcanic fields (regions of distributed volcanism) where low eruption rates, fertile soil, and attractive landscapes draw populations to live close by. Forecasting future activity in volcanic fields almost invariably uses spatial or spatio-temporal point processes with model selection and development based on exploratory analyses of previous eruption data. For identifiability reasons, spatio-temporal processes, and practically also spatial processes, the definition of a spatial region is required to which volcanism is confined. However, due to the complex and predominantly unknown sub-surface processes driving volcanic eruptions, definition of a region based solely on geological information is currently impossible. Thus, the current approach is to fit a shape to the known previous eruption sites. The class of boundary shape is an unavoidable subjective decision taken by the forecaster that is often overlooked during subsequent analysis of results. This study shows the substantial effect that this choice may have on even the simplest exploratory methods for hazard forecasting, illustrated using four commonly used exploratory statistical methods and two very different regions: the Auckland Volcanic Field, New Zealand, and Harrat Rahat, Kingdom of Saudi Arabia. For Harrat Rahat, sensitivity of results to boundary definition is substantial. For the Auckland Volcanic Field, the range of options resulted in similar shapes, nevertheless, some of the statistical tests still showed substantial variation in results. This work highlights the fact that when carrying out any hazard analysis on volcanic fields, it is vital to specify how the volcanic field boundary has been defined, assess the sensitivity of boundary choice, and to carry these assumptions and related uncertainties through to estimates of future activity and hazard analyses.

Evolution of damage during deformation in porous granular materials (Louis Néel Medal Lecture)

NASA Astrophysics Data System (ADS)

Main, Ian

2014-05-01

'Crackling noise' occurs in a wide variety of systems that respond to external forcing in an intermittent way, leading to sudden bursts of energy release similar to those heard when crunching up a piece of paper or listening to a fire. In mineral magnetism ('Barkhausen') crackling noise occurs due to sudden changes in the size and orientation of microscopic ferromagnetic domains when the external magnetic field is changed. In rock physics sudden changes in internal stress associated with microscopically brittle failure events lead to acoustic emissions that can be recorded on the sample boundary, and used to infer the state of internal damage. Crackling noise is inherently stochastic, but the population of events often exhibits remarkably robust scaling properties, in terms of the source area, duration, energy, and in the waiting time between events. Here I describe how these scaling properties emerge and evolve spontaneously in a fully-dynamic discrete element model of sedimentary rocks subject to uniaxial compression at a constant strain rate. The discrete elements have structural disorder similar to that of a real rock, and this is the only source of heterogeneity. Despite the stationary loading and the lack of any time-dependent weakening processes, the results are all characterized by emergent power law distributions over a broad range of scales, in agreement with experimental observation. As deformation evolves, the scaling exponents change systematically in a way that is similar to the evolution of damage in experiments on real sedimentary rocks. The potential for real-time failure forecasting is examined by using synthetic and real data from laboratory tests and prior to volcanic eruptions. The combination of non-linearity and an irreducible stochastic component leads to significant variations in the precision and accuracy of the forecast failure time, leading to a significant proportion of 'false alarms' (forecast too early) and 'missed events' (forecast too late), as well as an over-optimistic assessments of forecasting power and quality when the failure time is known (the 'benefit of hindsight'). The evolution becomes progressively more complex, and the forecasting power diminishes, in going from ideal synthetics to controlled laboratory tests to open natural systems at larger scales in space and time.

Towards a Local-Scale Climate Service for Colombian Agriculture: Findings and Future Perspectives

NASA Astrophysics Data System (ADS)

Ramirez-Villegas, J.; Prager, S.; Llanos, L.; Agudelo, D.; Esquivel, A.; Sotelo, S.; Guevara, E.; Howland, F. C.; Munoz, A.; Rodriguez, J.; Ordonez, L.; Fernandes, K.

2017-12-01

Globally, interannual climate variability explains roughly a third of the yield variation for major crops. In Colombia, interannual climate variations and specially those driven by ENSO can disrupt production, lower farmers' incomes and increase market prices for both urban and rural consumers alike. Farmers in Colombia, however, often plan for the cropping season based on the immediately prior year's experience, which is unlikely to result in successful crops under high climate variability events. Critical decisions for avoiding total investment loss or to ensure successful harvests, including issues related to planting date, what variety to plant, or whether to plant, are made, at best, intuitively. Here, we demonstrate that the combination of better data, skillful seasonal climate forecasts, calibrated crop models, and a web-based climate services platform tailored to users' needs can prove successful in establishing a sustained climate service for agriculture. Rainfall predictability analyses indicate that statistical seasonal climate forecasts are skillful enough for issuing forecasts reliably in virtually all areas, with dry periods generally showing greater predictability than wet periods. Importantly, we find that a better specification of predictor regions significantly enhances seasonal forecast skill. Rice and maize crop models capture well the growth and development of rice and maize crops in experimental settings, and ably simulate historical (1980-2014) variations in productivity. With skillful climate and crop models, we developed a climate services platform that produces seasonal climate forecasts, and connects these with crop models. A usability study of the forecast platform revealed that, from a population of ca. 200 farmers and professionals, roughly two thirds correctly interpreted information and felt both confident and encouraged to use the platform. Nevertheless, capacity strengthening on key agro-climatology concepts was highlighted by farmers as a crucial need. Challenges also arose in certain zones due to limited access to electricity, computers or Internet. Based on our results, we conclude that for a climate service to be truly sustainable, well-calibrated and skillful models are as critical as the co-creation of the service itself with the stakeholder community.

Using phenomenological models for forecasting the 2015 Ebola challenge.

PubMed

Pell, Bruce; Kuang, Yang; Viboud, Cecile; Chowell, Gerardo

2018-03-01

The rising number of novel pathogens threatening the human population has motivated the application of mathematical modeling for forecasting the trajectory and size of epidemics. We summarize the real-time forecasting results of the logistic equation during the 2015 Ebola challenge focused on predicting synthetic data derived from a detailed individual-based model of Ebola transmission dynamics and control. We also carry out a post-challenge comparison of two simple phenomenological models. In particular, we systematically compare the logistic growth model and a recently introduced generalized Richards model (GRM) that captures a range of early epidemic growth profiles ranging from sub-exponential to exponential growth. Specifically, we assess the performance of each model for estimating the reproduction number, generate short-term forecasts of the epidemic trajectory, and predict the final epidemic size. During the challenge the logistic equation consistently underestimated the final epidemic size, peak timing and the number of cases at peak timing with an average mean absolute percentage error (MAPE) of 0.49, 0.36 and 0.40, respectively. Post-challenge, the GRM which has the flexibility to reproduce a range of epidemic growth profiles ranging from early sub-exponential to exponential growth dynamics outperformed the logistic growth model in ascertaining the final epidemic size as more incidence data was made available, while the logistic model underestimated the final epidemic even with an increasing amount of data of the evolving epidemic. Incidence forecasts provided by the generalized Richards model performed better across all scenarios and time points than the logistic growth model with mean RMS decreasing from 78.00 (logistic) to 60.80 (GRM). Both models provided reasonable predictions of the effective reproduction number, but the GRM slightly outperformed the logistic growth model with a MAPE of 0.08 compared to 0.10, averaged across all scenarios and time points. Our findings further support the consideration of transmission models that incorporate flexible early epidemic growth profiles in the forecasting toolkit. Such models are particularly useful for quickly evaluating a developing infectious disease outbreak using only case incidence time series of the early phase of an infectious disease outbreak. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

The meta-Gaussian Bayesian Processor of forecasts and associated preliminary experiments

NASA Astrophysics Data System (ADS)

Chen, Fajing; Jiao, Meiyan; Chen, Jing

2013-04-01

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.

Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: a case study in endemic districts of Bhutan.

PubMed

Wangdi, Kinley; Singhasivanon, Pratap; Silawan, Tassanee; Lawpoolsri, Saranath; White, Nicholas J; Kaewkungwal, Jaranit

2010-09-03

Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX. This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month. It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and (1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010, where population in 2009 was 285,375 and the expected population of 2010 to be 289,085. The ARIMAX model of monthly cases and climatic factors showed considerable variations among the different districts. In general, the mean maximum temperature lagged at one month was a strong positive predictor of an increased malaria cases for four districts. The monthly number of cases of the previous month was also a significant predictor in one district, whereas no variable could predict malaria cases for two districts. The ARIMA models of time-series analysis were useful in forecasting the number of cases in the endemic areas of Bhutan. There was no consistency in the predictors of malaria cases when using ARIMAX model with selected lag times and climatic predictors. The ARIMA forecasting models could be employed for planning and managing malaria prevention and control programme in Bhutan.

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast

[Characteristics of quantitative values of regional factors of exposure in the studied areas].

PubMed

Rakhmanin, Iu A; Shashina, T A; Ungurianu, T N; Novikov, S M; Skvortsova, N S; Matsiuk, A V; Legostaeva, T B; Antipanova, N A

2012-01-01

In the paper the results of a comparative evaluation of the Russian and the standard, recommended by US EPA, factors of population exposure in seven areas of different federal districts of Russia are presented. Concerning the adult population differences reach 3.5 times, for children (1-6 years) - 4.2 times. An example of the effect of regional differences and standard factors on levels of exposure and risk is considered. Promising areas for further research on regional factors to improve the accuracy and reliability of the forecast assessments of the risks to public health have been identified.

Forecasting Ecological Genomics: High-Tech Animal Instrumentation Meets High-Throughput Sequencing

PubMed Central

Shafer, Aaron B. A.; Northrup, Joseph M.; Wikelski, Martin; Wittemyer, George; Wolf, Jochen B. W.

2016-01-01

Recent advancements in animal tracking technology and high-throughput sequencing are rapidly changing the questions and scope of research in the biological sciences. The integration of genomic data with high-tech animal instrumentation comes as a natural progression of traditional work in ecological genetics, and we provide a framework for linking the separate data streams from these technologies. Such a merger will elucidate the genetic basis of adaptive behaviors like migration and hibernation and advance our understanding of fundamental ecological and evolutionary processes such as pathogen transmission, population responses to environmental change, and communication in natural populations. PMID:26745372

a 24/7 High Resolution Storm Surge, Inundation and Circulation Forecasting System for Florida Coast

NASA Astrophysics Data System (ADS)

Paramygin, V.; Davis, J. R.; Sheng, Y.

2012-12-01

A 24/7 forecasting system for Florida is needed because of the high risk of tropical storm surge-induced coastal inundation and damage, and the need to support operational management of water resources, utility infrastructures, and fishery resources. With the anticipated climate change impacts, including sea level rise, coastal areas are facing the challenges of increasing inundation risk and increasing population. Accurate 24/7 forecasting of water level, inundation, and circulation will significantly enhance the sustainability of coastal communities and environments. Supported by the Southeast Coastal Ocean Observing Regional Association (SECOORA) through NOAA IOOS, a 24/7 high-resolution forecasting system for storm surge, coastal inundation, and baroclinic circulation is being developed for Florida using CH3D Storm Surge Modeling System (CH3D-SSMS). CH3D-SSMS is based on the CH3D hydrodynamic model coupled to a coastal wave model SWAN and basin scale surge and wave models. CH3D-SSMS has been verified with surge, wave, and circulation data from several recent hurricanes in the U.S.: Isabel (2003); Charley, Dennis and Ivan (2004); Katrina and Wilma (2005); Ike and Fay (2008); and Irene (2011), as well as typhoons in the Pacific: Fanapi (2010) and Nanmadol (2011). The effects of tropical cyclones on flow and salinity distribution in estuarine and coastal waters has been simulated for Apalachicola Bay as well as Guana-Tolomato-Matanzas Estuary using CH3D-SSMS. The system successfully reproduced different physical phenomena including large waves during Ivan that damaged I-10 Bridges, a large alongshore wave and coastal flooding during Wilma, salinity drop during Fay, and flooding in Taiwan as a result of combined surge and rain effect during Fanapi. The system uses 4 domains that cover entire Florida coastline: West, which covers the Florida panhandle and Tampa Bay; Southwest spans from Florida Keys to Charlotte Harbor; Southeast, covering Biscayne Bay and Miami and East, which continues north to the Florida/Georgia border. The system has a data acquisition and processing module that is used to collect data for model runs (e.g. wind, river flow, precipitation). Depending on the domain, forecasts runs can take ~1-18 hours to complete on a single CPU (8-core) system (1-2 hrs for 2D setup and up to 18 hrs for a 3D setup) with 4 forecasts generated per day. All data is archived / catalogued and model forecast skill is continuously being evaluated. In addition to the baseline forecasts, additional forecasts are being perform using various options for wind forcing (GFS, GFDL, WRF, and parametric hurricane models), model configurations (2D/ 3D), and open boundary conditions by coupling with large scale models (ROMS, NCOM, HYCOM), as well as incorporating real-time and forecast river flow and precipitation data to better understand how to improve model skill. In addition, new forecast products (e.g. more informative inundation maps) are being developed to targeted stakeholders. To support modern data standards, CH3D-SSMS results are available online via a THREDDS server in CF-Compliant NetCDF format as well as other stakeholder-friendly (e.g. GIS) formats. The SECOORA website provides visualization of the model via GODIVA-THREDDS interface.

Weather forecasting expert system study

NASA Technical Reports Server (NTRS)

1985-01-01

Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

Climate change threatens polar bear populations: a stochastic demographic analysis.

PubMed

Hunter, Christine M; Caswell, Hal; Runge, Michael C; Regehr, Eric V; Amstrup, Steve C; Stirling, Ian

2010-10-01

The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture-recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001-2003) and population decline in years with less ice coverage (2004-2005). LTRE (life table response experiment) analysis showed that the reduction in lambda in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log lambdas, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log lambdas approximately - 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with "business as usual" (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act.

Climate change threatens polar bear populations: A stochastic demographic analysis

USGS Publications Warehouse

Hunter, C.M.; Caswell, H.; Runge, M.C.; Regehr, E.V.; Amstrup, Steven C.; Stirling, I.

2010-01-01

The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture-recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001-2003) and population decline in years with less ice coverage (2004-2005). LTRE (life table response experiment) analysis showed that the reduction in ?? in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log ??s, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log ??s ' - 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with "business as usual" (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act. ?? 2010 by the Ecological Society of America.

Land use and water use in the Antelope Valley, California

USGS Publications Warehouse

Templin, William E.; Phillips, Steven P.; Cherry, Daniel E.; DeBortoli, Myrna L.; Haltom, T.C.; McPherson, Kelly R.; Mrozek, C.A.

1995-01-01

Urban land use and water use in the Antelope Valley, California, have increased significantly since development of the valley began in the late 1800's.. Ground water has been a major source of water in this area because of limited local surface-water resources. Ground-water pumpage is reported to have increased from about 29,000 acre-feet in 1919 to about 400,000 acre-feet in the 1950's. Completion of the California Aqueduct to this area in the early 1970's conveyed water from the Sacramento-San Joaquin Delta, about 400 miles to the north. Declines in groundwater levels and increased costs of electrical power in the 1970's resulted in a reduction in the quantity of ground water that was pumped annually for irrigation uses. Total annual reported ground-water pumpage decreased to a low of about 53,200 acre-feet in 1983 and increased to about 91,700 acre-feet in 1991 as a result of rapid urban development and the 1987-92 drought. This increased urban development, in combination with several years of drought, renewed concern about a possible return to extensive depletion of ground-water storage and increased land subsidence.Increased water demands are expected to continue as a result of increased urban development. Water-demand forecasts in 1980 for the Antelope Valley indicated that total annual water demand by 2020 was expected to be about 250,000 acre-feet, with agricultural demand being about 65 percent of this total. In 1990, total water demand was projected to be about 175,000 acre-feet by 2010; however, agricultural water demand was expected to account for only 37 percent of the total demand. New and existing land- and water-use data were collected and compiled during 1992-93 to identify present and historical land and water uses. In 1993, preliminary forecasts for total water demand by 2010 ranged from about 127,500 to 329,000 acre-feet. These wide-ranging estimates indicate that forecasts can change with time as factors that affect water demand change and different forecasting methods are used. The forecasts using the MWD_MAIN (Metropolitan Water District of Southern California Municipal and Industrial Needs) water-demand forecasting system yielded the largest estimates of water demand. These forecasts were based on projections of population growth and other socioeconomic variables. Initial forecasts using the MWD_MAIN forecasting system commonly are considered "interim" or preliminary. Available historical and future socioeconomic data required for the forecasting system are limited for this area. Decisions on local water-resources demand management may be made by members of the Antelope Valley Water Group and other interested parties based on this report, other studies, their best judgement, and cumulative knowledge of local conditions. Potential water-resource management actions in the Antelope Valley include (1) increasing artificial ground-water recharge when excess local runoff (or imported water supplies) are available; (2) implementing water-conservation best-management practices; and (3) optimizing ground-water pumpage throughout the basin.

Early forecasting of Indian Summer Monsoon: case study 2016

NASA Astrophysics Data System (ADS)

Surovyatkina, Elena; Stolbova, Veronika; Kurths, Jurgen

2017-04-01

The prior knowledge of dates of onset and withdrawal of monsoon is of vital importance for the population of the Indian subcontinent. In May 2016 before monsoon season, India recorded its highest-ever temperature of 51C. Hot waves have decimated crops, killed livestock and left 330 million people without enough water. At the end of monsoon season the floods in Indian this year have also broken previous records. Severe and devastating rainfall poured down, triggering dams spilling and floods. Such extreme conditions pose the vital questions such as: When will the monsoon come? When will the monsoon withdraw? More lead time in monsoon forecast warning is crucial for taking appropriate decisions at various levels - from the farmer's field (e.g. plowing day, seeding) to the central government (e.g. managing water and energy resources, food procurement policies). The Indian Meteorological Department issues forecasts of onset of monsoon for Kerala state in South India on May 15-th. It does not give such predictions for the other 28 states of the country. Our study concerns the central part of India. We made the monsoon forecast using our recently developed method which focuses on Tipping elements of the Indian monsoon [1]. Our prediction relies on observations of near-surface air temperature and relative humidity from both the ERA-40 and NCEP/NCAR reanalyses. We performed both of our forecasts for the onset and withdrawal of monsoon for the central part of India, the Eastern Ghats (20N,80E). We predicted the monsoon arrival to the Eastern Ghats (20N,80E) on the 13th of June with a deviation of +/-4 days. The prediction was made on May 6-th, 2016 [2], that is 40 days in advance of the date of the forecast. The actual monsoon arrival was June 17-th. In this day near-surface air temperature and relative humidity overcame the critical values and the monsoon season started, that was confirmed by observations of meteorological stations located around the EG-region. We forecasted the monsoon withdrawal from the Eastern Ghats on the 5th of October with a deviation of +/-5 days. We delivered this prediction on July 27-th, 2016 [3], namely 70 days in advance. The date of the actual start of monsoon withdrawal was October 10th. In this day relative humidity began to decrease. Then it passed the 80 percent threshold, and a transition back to a monsoon became impossible, meteorological stations registered it also. We emphasize that our forecasts of the monsoon onset and withdrawal were delivered for 40 and 70 days in advance respectively, and both of our forecasts lie within our prediction interval. Hence, this year we proved that such early prediction of the monsoon timing is possible. [1] Stolbova, V., E. Surovyatkina, B. Bookhagen, and J. Kurths (2016): Tipping elements of the Indian monsoon: Prediction of onset and withdrawal. Geophys. Res. Lett., 43, 1-9 [doi:10.1002/2016GL068392] [2]https://www.pik-potsdam.de/news/press-releases/indian-monsoon-novel-approach-allows-early-forecasting?set_language=en [3] https://www.pik-potsdam.de/kontakt/pressebuero/fotos/monsoon-withdrawal/view

Adjustment factors to per capita health-care indicators in countries with expatriate male-majority populations.

PubMed

Hussin, A H

2014-12-17

From 2000 to 2010, the population in the Gulf Cooperation Council (GCC) countries underwent an increase of 53%, compared with an average global increase of 13%. The rates varied by country, ranging from 23% in Oman to 198% in Qatar. The main driving force for this sharp increase in population was the high demand for immigrant labour. The aim of this study was to adjust the population in the GCC countries in order to ensure that the comparisons of health-care key performance indicators with other countries account for the composition of the populations. The conclusion of the study was that adjusting the population in the GCC is instrumental for determining health spending and health outcomes, and that inaccurate forecasting would result in serious overestimation of the need for GCC countries to invest in the health-care sector. Policy-makers can utilize the population models in this study to accurately plan for health-care delivery.

Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Jain, Rahul; Vaughan, Joseph; Heitkamp, Kyle; Ramos, Charleston; Claiborn, Candis; Schreuder, Maarten; Schaaf, Mark; Lamb, Brian

The post-harvest burning of agricultural fields is commonly used to dispose of crop residue and provide other desired services such as pest control. Despite careful regulation of burning, smoke plumes from field burning in the Pacific Northwest commonly degrade air quality, particularly for rural populations. In this paper, ClearSky, a numerical smoke dispersion forecast system for agricultural field burning that was developed to support smoke management in the Inland Pacific Northwest, is described. ClearSky began operation during the summer through fall burn season of 2002 and continues to the present. ClearSky utilizes Mesoscale Meteorological Model version 5 (MM5v3) forecasts from the University of Washington, data on agricultural fields, a web-based user interface for defining burn scenarios, the Lagrangian CALPUFF dispersion model and web-served animations of plume forecasts. The ClearSky system employs a unique hybrid source configuration, which treats the flaming portion of a field as a buoyant line source and the smoldering portion of the field as a buoyant area source. Limited field observations show that this hybrid approach yields reasonable plume rise estimates using source parameters derived from recent field burning emission field studies. The performance of this modeling system was evaluated for 2003 by comparing forecast meteorology against meteorological observations, and comparing model-predicted hourly averaged PM 2.5 concentrations against observations. Examples from this evaluation illustrate that while the ClearSky system can accurately predict PM 2.5 surface concentrations due to field burning, the overall model performance depends strongly on meteorological forecast error. Statistical evaluation of the meteorological forecast at seven surface stations indicates a strong relationship between topographical complexity near the station and absolute wind direction error with wind direction errors increasing from approximately 20° for sites in open areas to 70° or more for sites in very complex terrain. The analysis also showed some days with good forecast meteorology with absolute mean error in wind direction less than 30° when ClearSky correctly predicted PM 2.5 surface concentrations at receptors affected by field burns. On several other days with similar levels of wind direction error the model did not predict apparent plume impacts. In most of these cases, there were no reported burns in the vicinity of the monitor and, thus, it appeared that other, non-reported burns were responsible for the apparent plume impact at the monitoring site. These cases do not provide information on the performance of the model, but rather indicate that further work is needed to identify all burns and to improve burn reports in an accurate and timely manner. There were also a number of days with wind direction errors exceeding 70° when the forecast system did not correctly predict plume behavior.

The future of life expectancy and life expectancy inequalities in England and Wales: Bayesian spatiotemporal forecasting

PubMed Central

Bennett, James E; Li, Guangquan; Foreman, Kyle; Best, Nicky; Kontis, Vasilis; Pearson, Clare; Hambly, Peter; Ezzati, Majid

2015-01-01

Summary Background To plan for pensions and health and social services, future mortality and life expectancy need to be forecast. Consistent forecasts for all subnational units within a country are very rare. Our aim was to forecast mortality and life expectancy for England and Wales' districts. Methods We developed Bayesian spatiotemporal models for forecasting of age-specific mortality and life expectancy at a local, small-area level. The models included components that accounted for mortality in relation to age, birth cohort, time, and space. We used geocoded mortality and population data between 1981 and 2012 from the Office for National Statistics together with the model with the smallest error to forecast age-specific death rates and life expectancy to 2030 for 375 of England and Wales' 376 districts. We measured model performance by withholding recent data and comparing forecasts with this withheld data. Findings Life expectancy at birth in England and Wales was 79·5 years (95% credible interval 79·5–79·6) for men and 83·3 years (83·3–83·4) for women in 2012. District life expectancies ranged between 75·2 years (74·9–75·6) and 83·4 years (82·1–84·8) for men and between 80·2 years (79·8–80·5) and 87·3 years (86·0–88·8) for women. Between 1981 and 2012, life expectancy increased by 8·2 years for men and 6·0 years for women, closing the female–male gap from 6·0 to 3·8 years. National life expectancy in 2030 is expected to reach 85·7 (84·2–87·4) years for men and 87·6 (86·7–88·9) years for women, further reducing the female advantage to 1·9 years. Life expectancy will reach or surpass 81·4 years for men and reach or surpass 84·5 years for women in every district by 2030. Longevity inequality across districts, measured as the difference between the 1st and 99th percentiles of district life expectancies, has risen since 1981, and is forecast to rise steadily to 8·3 years (6·8–9·7) for men and 8·3 years (7·1–9·4) for women by 2030. Interpretation Present forecasts underestimate the expected rise in life expectancy, especially for men, and hence the need to provide improved health and social services and pensions for elderly people in England and Wales. Health and social policies are needed to curb widening life expectancy inequalities, help deprived districts catch up in longevity gains, and avoid a so-called grand divergence in health and longevity. Funding UK Medical Research Council and Public Health England. PMID:25935825

A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

NASA Astrophysics Data System (ADS)

Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

2017-04-01

Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land surface model-flood inundation model to produce hydrological variables and indices at daily, 0.25-degree resolution, globally. The system is updated in near real-time (< 2 days) using satellite precipitation and weather model data, and produces forecasts at short-term (out to 7 days) based on the Global Forecast System (GFS) and seasonal (up to 6 months) based on U.S. National Multi-Model Ensemble (NMME) seasonal forecasts. Climate change projections are based on bias-corrected and downscaled CMIP5 climate data that is used to force the hydrological model. Example products from the system include real-time and forecast drought indices for precipitation, soil moisture, and streamflow, and flood magnitude and extent indices. The model outputs are complemented by satellite based products and indices based on satellite data for vegetation health (MODIS NDVI) and soil moisture (SMAP). We show examples of the validation of the system at regional scales, including how local information can significantly improve predictions, and examples of how the system can be used to understand large-scale water resource issues, and in real-world contexts for early warning, decision making and planning.

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoff, Thomas Hoff; Kankiewicz, Adam

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP)more » forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”« less

The Changing Face of Poverty. Trends in New York City's Population in Poverty: 1960-1990.

ERIC Educational Resources Information Center

Tobier, Emanuel

This report is the first product of the Community Service Society's Economic and Social Monitoring Unit which analyzes and forecasts the status of the poor in New York City (NYC). The report documents the following major findings: (1) nationwide, there are now fewer elderly and more minorities and women among the poor; the trends in New York City…

Looking Forward: Visions of the Future of the South. The 1984 Report of the Committee on Southern Trends of the Southern Growth Policies Board.

ERIC Educational Resources Information Center

Rosenfeld, Stuart A., Ed.

Written for Southern policymakers, this report forecasts economic changes in the South. It addresses demographic factors, traditional economic concerns, and emerging economic realities which already influence, or are likely to influence, economic life in the South. Trends and issues include: (1) the aging of the population largely due to retirees…

Public Investment and the Goal of Providing Universal Access to Primary Education by 2015 in Kenya

ERIC Educational Resources Information Center

Omwami, Edith Mukudi; Omwami, Raymond K.

2010-01-01

The authors use population census data to project school enrolment for Kenya. They also employ current education sector budget and national revenue base statistics to model the sector budget and to forecast the revenue base growth required to sustain universal primary education (UPE). The 2003 fiscal year unit cost of education is used as the base…

The Move to Mobile: Where Is a Campus's Place in the Mobile Space?

ERIC Educational Resources Information Center

Lum, Lydia

2012-01-01

At the end of 2010, for the first time ever, smartphones outsold PCs. Mobile device adoption rates continue to rise rapidly around the world. A recent forecast by Cisco found that global mobile data traffic more than doubled last year, and by the end of 2012, the number of mobile devices in use will outnumber the world's population. In the United…

Physician and nurse supply in Serbia using time-series data

PubMed Central

2013-01-01

Background Unemployment among health professionals in Serbia has risen in the recent past and continues to increase. This highlights the need to understand how to change policies to meet real and projected needs. This study identified variables that were significantly related to physician and nurse employment rates in the public healthcare sector in Serbia from 1961 to 2008 and used these to develop parameters to model physician and nurse supply in the public healthcare sector through to 2015. Methods The relationships among six variables used for planning physician and nurse employment in public healthcare sector in Serbia were identified for two periods: 1961 to 1982 and 1983 to 2008. Those variables included: the annual total national population; gross domestic product adjusted to 1994 prices; inpatient care discharges; outpatient care visits; students enrolled in the first year of medical studies at public universities; and the annual number of graduated physicians. Based on historic trends, physician supply and nurse supply in the public healthcare sector by 2015 (with corresponding 95% confidence level) have been modeled using Autoregressive Integrated Moving Average (ARIMA) / Transfer function (TF) models. Results The ARIMA/TF modeling yielded stable and significant forecasts of physician supply (stationary R2 squared = 0.71) and nurse supply (stationary R2 squared = 0.92) in the public healthcare sector in Serbia through to 2015. The most significant predictors for physician employment were the population and GDP. The supply of nursing staff was, in turn, related to the number of physicians. Physician and nurse rates per 100,000 population increased by 13%. The model predicts a seven-year mismatch between the supply of graduates and vacancies in the public healthcare sector is forecasted at 8,698 physicians - a net surplus. Conclusion The ARIMA model can be used to project trends, especially those that identify significant mismatches between forecasted supply of physicians and vacancies and can be used to guide decision-making for enrollment planning for the medical schools in Serbia. Serbia needs an inter-sectoral strategy for HRH development that is more coherent with healthcare objectives and more accountable in terms of professional mobility. PMID:23773678

Physician and nurse supply in Serbia using time-series data.

PubMed

Santric-Milicevic, Milena; Vasic, Vladimir; Marinkovic, Jelena

2013-06-17

Unemployment among health professionals in Serbia has risen in the recent past and continues to increase. This highlights the need to understand how to change policies to meet real and projected needs. This study identified variables that were significantly related to physician and nurse employment rates in the public healthcare sector in Serbia from 1961 to 2008 and used these to develop parameters to model physician and nurse supply in the public healthcare sector through to 2015. The relationships among six variables used for planning physician and nurse employment in public healthcare sector in Serbia were identified for two periods: 1961 to 1982 and 1983 to 2008. Those variables included: the annual total national population; gross domestic product adjusted to 1994 prices; inpatient care discharges; outpatient care visits; students enrolled in the first year of medical studies at public universities; and the annual number of graduated physicians. Based on historic trends, physician supply and nurse supply in the public healthcare sector by 2015 (with corresponding 95% confidence level) have been modeled using Autoregressive Integrated Moving Average (ARIMA) / Transfer function (TF) models. The ARIMA/TF modeling yielded stable and significant forecasts of physician supply (stationary R2 squared = 0.71) and nurse supply (stationary R2 squared = 0.92) in the public healthcare sector in Serbia through to 2015. The most significant predictors for physician employment were the population and GDP. The supply of nursing staff was, in turn, related to the number of physicians. Physician and nurse rates per 100,000 population increased by 13%. The model predicts a seven-year mismatch between the supply of graduates and vacancies in the public healthcare sector is forecasted at 8,698 physicians - a net surplus. The ARIMA model can be used to project trends, especially those that identify significant mismatches between forecasted supply of physicians and vacancies and can be used to guide decision-making for enrollment planning for the medical schools in Serbia. Serbia needs an inter-sectoral strategy for HRH development that is more coherent with healthcare objectives and more accountable in terms of professional mobility.

Toward a coupled Hazard-Vulnerability Tool for Flash Flood Impacts Prediction

NASA Astrophysics Data System (ADS)

Terti, Galateia; Ruin, Isabelle; Anquetin, Sandrine; Gourley, Jonathan J.

2015-04-01

Flash floods (FF) are high-impact, catastrophic events that result from the intersection of hydrometeorological extremes and society at small space-time scales, generally on the order of minutes to hours. Because FF events are generally localized in space and time, they are very difficult to forecast with precision and can subsequently leave people uninformed and subject to surprise in the midst of their daily activities (e.g., commuting to work). In Europe, FFs are the main source of natural hazard fatalities, although they affect smaller areas than riverine flooding. In the US, also, flash flooding is the leading cause of weather-related deaths most years, with some 200 annual fatalities. There were 954 fatalities and approximately 31 billion U.S. dollars of property damage due to floods and flash floods from 1995 to 2012 in the US. For forecasters and emergency managers the prediction of and subsequent response to impacts due to such a sudden onset and localized event remains a challenge. This research is motivated by the hypothesis that the intersection of the spatio-temporal context of the hazard with the distribution of people and their characteristics across space and time reveals different paths of vulnerability. We argue that vulnerability and the dominant impact type varies dynamically throughout the day and week according to the location under concern. Thus, indices are appropriate to develop and provide, for example, vehicle-related impacts on active population being focused on the road network during morning or evening rush hours. This study describes the methodological developments of our approach and applies our hypothesis to the case of the June 14th, 2010 flash flood event in the Oklahoma City area (Oklahoma, US). Social (i.e. population socio-economic profile), exposure (i.e. population distribution, land use), and physical (i.e. built and natural environment) data are used to compose different vulnerability products based on the forecast location and timing of the specific FF occurrence. Contingent index-based impact maps are then derived from the intersection of the hydro-meteorological indices with the exposure, sensitivity and/or coping capacity indices describing the infrastructure and people in the study area.

Optimising seasonal streamflow forecast lead time for operational decision making in Australia

NASA Astrophysics Data System (ADS)

Schepen, Andrew; Zhao, Tongtiegang; Wang, Q. J.; Zhou, Senlin; Feikema, Paul

2016-10-01

Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0-14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to -15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.

Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

NASA Astrophysics Data System (ADS)

Christensen, Hannah; Moroz, Irene; Palmer, Tim

2015-04-01

Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.

Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.

In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based onmore » a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.« less

Improved Anvil Forecasting

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.

2000-01-01

This report describes the outcome of Phase 1 of the AMU's Improved Anvil Forecasting task. Forecasters in the 45th Weather Squadron and the Spaceflight Meteorology Group have found that anvil forecasting is a difficult task when predicting LCC and FR violations. The purpose of this task is to determine the technical feasibility of creating an anvil-forecasting tool. Work on this study was separated into three steps: literature search, forecaster discussions, and determination of technical feasibility. The literature search revealed no existing anvil-forecasting techniques. However, there appears to be growing interest in anvils in recent years. If this interest continues to grow, more information will be available to aid in developing a reliable anvil-forecasting tool. The forecaster discussion step revealed an array of methods on how better forecasting techniques could be developed. The forecasters have ideas based on sound meteorological principles and personal experience in forecasting and analyzing anvils. Based on the information gathered in the discussions with the forecasters, the conclusion of this report is that it is technically feasible at this time to develop an anvil forecasting technique that will significantly contribute to the confidence in anvil forecasts.

Cod Collapse and Climate in the North Atlantic

NASA Astrophysics Data System (ADS)

Oremus, K. L.; Meng, K. C.; Gaines, S.

2016-02-01

Understanding the determinants of fish population dynamics is crucial to the recovery of many fisheries. Current research emphasizes the role of environmental conditions in driving fish populations, but the magnitude of and mechanisms behind these effects on crucial populations are not well established. Despite aggressive management efforts, New England cod fisheries have been in decline for several decades and have now reached unprecedented lows. We find a strong negative relationship between the North Atlantic Oscillation (NAO) and subsequent adult cod biomass and catch. In the Gulf of Maine fishery, a 1-unit NAO increase is associated with a 13% decrease in the biomass of age-1 cod the following year, a decrease that persists as the affected cohort matures. We further detect that a 1-unit NAO increase can lower commercial catch for up to 19 subsequent years, suggesting that fishing practices may be inadvertently exacerbating NAO's direct biological effects. These results imply that 18% and 32% of the overall decline in adult biomass and catch, respectively, since 1980 can be attributed to the NAO's recent multi-decadal positive phase. The Georges Bank cod fishery displays similar patterns. Because there is a delay between an NAO event and subsequent declines in adult biomass, our finding implies that already observed NAO events can be used in stock forecasts, providing lead time for adaptive policy. More broadly, our approach can inform forecasting efforts for other fisheries strongly affected by natural and anthropogenic climatic variation.

An Approach to Forecasting Health Expenditures, with Application to the U.S. Medicare System

PubMed Central

Lee, Ronald; Miller, Timothy

2002-01-01

Objective To quantify uncertainty in forecasts of health expenditures. Study Design Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death. Data Sources/Study Setting A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example. Principal Findings Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita. Conclusions History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high–low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP. PMID:12479501

Future Economics of Liver Transplantation: A 20-Year Cost Modeling Forecast and the Prospect of Bioengineering Autologous Liver Grafts

PubMed Central

Habka, Dany; Mann, David; Landes, Ronald; Soto-Gutierrez, Alejandro

2015-01-01

During the past 20 years liver transplantation has become the definitive treatment for most severe types of liver failure and hepatocellular carcinoma, in both children and adults. In the U.S., roughly 16,000 individuals are on the liver transplant waiting list. Only 38% of them will receive a transplant due to the organ shortage. This paper explores another option: bioengineering an autologous liver graft. We developed a 20-year model projecting future demand for liver transplants, along with costs based on current technology. We compared these cost projections against projected costs to bioengineer autologous liver grafts. The model was divided into: 1) the epidemiology model forecasting the number of wait-listed patients, operated patients and postoperative patients; and 2) the treatment model forecasting costs (pre-transplant-related costs; transplant (admission)-related costs; and 10-year post-transplant-related costs) during the simulation period. The patient population was categorized using the Model for End-Stage Liver Disease score. The number of patients on the waiting list was projected to increase 23% over 20 years while the weighted average treatment costs in the pre-liver transplantation phase were forecast to increase 83% in Year 20. Projected demand for livers will increase 10% in 10 years and 23% in 20 years. Total costs of liver transplantation are forecast to increase 33% in 10 years and 81% in 20 years. By comparison, the projected cost to bioengineer autologous liver grafts is $9.7M based on current catalog prices for iPS-derived liver cells. The model projects a persistent increase in need and cost of donor livers over the next 20 years that’s constrained by a limited supply of donor livers. The number of patients who die while on the waiting list will reflect this ever-growing disparity. Currently, bioengineering autologous liver grafts is cost prohibitive. However, costs will decline rapidly with the introduction of new manufacturing strategies and economies of scale. PMID:26177505

Future Economics of Liver Transplantation: A 20-Year Cost Modeling Forecast and the Prospect of Bioengineering Autologous Liver Grafts.

PubMed

Habka, Dany; Mann, David; Landes, Ronald; Soto-Gutierrez, Alejandro

2015-01-01

During the past 20 years liver transplantation has become the definitive treatment for most severe types of liver failure and hepatocellular carcinoma, in both children and adults. In the U.S., roughly 16,000 individuals are on the liver transplant waiting list. Only 38% of them will receive a transplant due to the organ shortage. This paper explores another option: bioengineering an autologous liver graft. We developed a 20-year model projecting future demand for liver transplants, along with costs based on current technology. We compared these cost projections against projected costs to bioengineer autologous liver grafts. The model was divided into: 1) the epidemiology model forecasting the number of wait-listed patients, operated patients and postoperative patients; and 2) the treatment model forecasting costs (pre-transplant-related costs; transplant (admission)-related costs; and 10-year post-transplant-related costs) during the simulation period. The patient population was categorized using the Model for End-Stage Liver Disease score. The number of patients on the waiting list was projected to increase 23% over 20 years while the weighted average treatment costs in the pre-liver transplantation phase were forecast to increase 83% in Year 20. Projected demand for livers will increase 10% in 10 years and 23% in 20 years. Total costs of liver transplantation are forecast to increase 33% in 10 years and 81% in 20 years. By comparison, the projected cost to bioengineer autologous liver grafts is $9.7M based on current catalog prices for iPS-derived liver cells. The model projects a persistent increase in need and cost of donor livers over the next 20 years that's constrained by a limited supply of donor livers. The number of patients who die while on the waiting list will reflect this ever-growing disparity. Currently, bioengineering autologous liver grafts is cost prohibitive. However, costs will decline rapidly with the introduction of new manufacturing strategies and economies of scale.

Low validity of Google Trends for behavioral forecasting of national suicide rates.

PubMed

Tran, Ulrich S; Andel, Rita; Niederkrotenthaler, Thomas; Till, Benedikt; Ajdacic-Gross, Vladeta; Voracek, Martin

2017-01-01

Recent research suggests that search volumes of the most popular search engine worldwide, Google, provided via Google Trends, could be associated with national suicide rates in the USA, UK, and some Asian countries. However, search volumes have mostly been studied in an ad hoc fashion, without controls for spurious associations. This study evaluated the validity and utility of Google Trends search volumes for behavioral forecasting of suicide rates in the USA, Germany, Austria, and Switzerland. Suicide-related search terms were systematically collected and respective Google Trends search volumes evaluated for availability. Time spans covered 2004 to 2010 (USA, Switzerland) and 2004 to 2012 (Germany, Austria). Temporal associations of search volumes and suicide rates were investigated with time-series analyses that rigorously controlled for spurious associations. The number and reliability of analyzable search volume data increased with country size. Search volumes showed various temporal associations with suicide rates. However, associations differed both across and within countries and mostly followed no discernable patterns. The total number of significant associations roughly matched the number of expected Type I errors. These results suggest that the validity of Google Trends search volumes for behavioral forecasting of national suicide rates is low. The utility and validity of search volumes for the forecasting of suicide rates depend on two key assumptions ("the population that conducts searches consists mostly of individuals with suicidal ideation", "suicide-related search behavior is strongly linked with suicidal behavior"). We discuss strands of evidence that these two assumptions are likely not met. Implications for future research with Google Trends in the context of suicide research are also discussed.

Provincial Variation of Cochlear Implantation Surgical Volumes and Cost in Canada.

PubMed

Crowson, Matthew G; Chen, Joseph M; Tucci, Debara

2017-01-01

Objectives To investigate provincial cochlear implantation (CI) annual volume and cost trends. Study Design Database analysis. Setting National surgical volume and cost database. Subjects and Methods Aggregate-level provincial CI volumes and cost data for adult and pediatric CI surgery from 2005 to 2014 were obtained from the Canadian Institute for Health Information. Population-level aging forecast estimates were obtained from the Ontario Ministry of Finance and Statistics Canada. Linear fit, analysis of variance, and Tukey's analyses were utilized to compare variances and means. Results The national volume of annual CI procedures is forecasted to increase by <30 per year ( R 2 = 0.88). Ontario has the highest mean annual CI volume (282; 95% confidence interval, 258-308), followed by Alberta (92.0; 95% confidence interval, 66.3-118), which are significantly higher than all other provinces ( P < .05 for each). Ontario's annual CI procedure volume is forecasted to increase by <11 per year ( R 2 = 0.62). Newfoundland and Nova Scotia have the highest CI procedures per 100,000 residents as compared with all other provinces ( P < .05). Alberta, Newfoundland, and Manitoba have the highest estimated implantation cost of all provinces ( P < .05). Conclusions Historical trends of CI forecast modest national volume growth. Potential bottlenecks include provincial funding and access to surgical expertise. The proportion of older adult patients who may benefit from a CI will rise, and there may be insufficient capacity to meet this need. Delayed access to CI for pediatric patients is also a concern, given recent reports of long wait times for CI surgery.

Hydrological Predictability for the Peruvian Amazon

NASA Astrophysics Data System (ADS)

Towner, Jamie; Stephens, Elizabeth; Cloke, Hannah; Bazo, Juan; Coughlan, Erin; Zsoter, Ervin

2017-04-01

Population growth in the Peruvian Amazon has prompted the expansion of livelihoods further into the floodplain and thus increasing vulnerability to the annual rise and fall of the river. This growth has coincided with a period of increasing hydrological extremes with more frequent severe flood events. The anticipation and forecasting of these events is crucial for mitigating vulnerability. Forecast-based Financing (FbF) an initiative of the German Red Cross implements risk reducing actions based on threshold exceedance within hydrometeorological forecasts using the Global Flood Awareness System (GloFAS). However, the lead times required to complete certain actions can be long (e.g. several weeks to months ahead to purchase materials and reinforce houses) and are beyond the current capabilities of GloFAS. Therefore, further calibration of the model is required in addition to understanding the climatic drivers and associated hydrological response for specific flood events, such as those observed in 2009, 2012 and 2015. This review sets out to determine the current capabilities of the GloFAS model while exploring the limits of predictability for the Amazon basin. More specifically, how the temporal patterns of flow within the main coinciding tributaries correspond to the overall Amazonian flood wave under various climatic and meteorological influences. Linking the source areas of flow to predictability within the seasonal forecasting system will develop the ability to expand the limit of predictability of the flood wave. This presentation will focus on the Iquitos region of Peru, while providing an overview of the new techniques and current challenges faced within seasonal flood prediction.

Flare forecasting at the Met Office Space Weather Operations Centre

NASA Astrophysics Data System (ADS)

Murray, S. A.; Bingham, S.; Sharpe, M.; Jackson, D. R.

2017-04-01

The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end-users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms: forecasts for each active region on the solar disk over the next 24 h and full-disk forecasts for the next 4 days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results and forecasting skill decreasing over longer forecast lead times.

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be either an intermediate forecast between the extremes of the ensemble spread or a manually selected forecast based on a meteorologists advice. 2. Downstream catchments with low influence of weather forecast In downstream catchments with strong human impact on discharge (e.g. by reservoir operation) and large influence of upstream gauge observation quality on forecast quality, the 'overall error' may in most cases be larger than the combination of the 'model error' and an ensemble spread. Therefore, the overall forecast uncertainty bounds are calculated differently: a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. Here, additionally the corresponding inflow hydrograph from all upstream catchments must be used. b) As for an upstream catchment, the uncertainty range is determined by combination of 'model error' and the ensemble member forecasts c) In addition, the 'overall error' is superimposed on the 'lead forecast'. For reasons of consistency, the lead forecast must be based on the same meteorological forecast in the downstream and all upstream catchments. d) From the resulting two uncertainty ranges (one from the ensemble forecast and 'model error', one from the 'lead forecast' and 'overall error'), the envelope is taken as the most prudent uncertainty range. In sum, the uncertainty associated with each forecast run is calculated and communicated to the public in the form of 10% and 90% percentiles. As in part I of this study, the methodology as well as the useful- or uselessness of the resulting uncertainty ranges will be presented and discussed by typical examples.

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

Predicting debris

NASA Technical Reports Server (NTRS)

Kessler, Donald J.

1988-01-01

The probable amount, sizes, and relative velocities of debris are discussed, giving examples of the damage caused by debris, and focusing on the use of mathematical models to forecast the debris environment and solar activity now and in the future. Most debris are within 2,000 km of the earth's surface. The average velocity of spacecraft-debris collisions varies from 9 km/sec at 30 degrees of inclination to 13 km/sec near polar orbits. Mathematical models predict a 5 percent per year increase in the large-fragment population, producing a small-fragment population increase of 10 percent per year until the year 2060, the time of critical density. A 10 percent increase in the large population would cause the critical density to be reached around 2025.

Lessons of L'Aquila for Operational Earthquake Forecasting

NASA Astrophysics Data System (ADS)

Jordan, T. H.

2012-12-01

The L'Aquila earthquake of 6 Apr 2009 (magnitude 6.3) killed 309 people and left tens of thousands homeless. The mainshock was preceded by a vigorous seismic sequence that prompted informal earthquake predictions and evacuations. In an attempt to calm the population, the Italian Department of Civil Protection (DPC) convened its Commission on the Forecasting and Prevention of Major Risk (MRC) in L'Aquila on 31 March 2009 and issued statements about the hazard that were widely received as an "anti-alarm"; i.e., a deterministic prediction that there would not be a major earthquake. On October 23, 2012, a court in L'Aquila convicted the vice-director of DPC and six scientists and engineers who attended the MRC meeting on charges of criminal manslaughter, and it sentenced each to six years in prison. A few weeks after the L'Aquila disaster, the Italian government convened an International Commission on Earthquake Forecasting for Civil Protection (ICEF) with the mandate to assess the status of short-term forecasting methods and to recommend how they should be used in civil protection. The ICEF, which I chaired, issued its findings and recommendations on 2 Oct 2009 and published its final report, "Operational Earthquake Forecasting: Status of Knowledge and Guidelines for Implementation," in Aug 2011 (www.annalsofgeophysics.eu/index.php/annals/article/view/5350). As defined by the Commission, operational earthquake forecasting (OEF) involves two key activities: the continual updating of authoritative information about the future occurrence of potentially damaging earthquakes, and the officially sanctioned dissemination of this information to enhance earthquake preparedness in threatened communities. Among the main lessons of L'Aquila is the need to separate the role of science advisors, whose job is to provide objective information about natural hazards, from that of civil decision-makers who must weigh the benefits of protective actions against the costs of false alarms and failures-to-predict. The best way to achieve this separation is to use probabilistic rather than deterministic statements in characterizing short-term changes in seismic hazards. The ICEF recommended establishing OEF systems that can provide the public with open, authoritative, and timely information about the short-term probabilities of future earthquakes. Because the public needs to be educated into the scientific conversation through repeated communication of probabilistic forecasts, this information should be made available at regular intervals, during periods of normal seismicity as well as during seismic crises. In an age of nearly instant information and high-bandwidth communication, public expectations regarding the availability of authoritative short-term forecasts are rapidly evolving, and there is a greater danger that information vacuums will spawn informal predictions and misinformation. L'Aquila demonstrates why the development of OEF capabilities is a requirement, not an option.

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.

PubMed

Brownstein, John S; Chu, Shuyu; Marathe, Achla; Marathe, Madhav V; Nguyen, Andre T; Paolotti, Daniela; Perra, Nicola; Perrotta, Daniela; Santillana, Mauricio; Swarup, Samarth; Tizzoni, Michele; Vespignani, Alessandro; Vullikanti, Anil Kumar S; Wilson, Mandy L; Zhang, Qian

2017-11-01

Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world. ©John S Brownstein, Shuyu Chu, Achla Marathe, Madhav V Marathe, Andre T Nguyen, Daniela Paolotti, Nicola Perra, Daniela Perrotta, Mauricio Santillana, Samarth Swarup, Michele Tizzoni, Alessandro Vespignani, Anil Kumar S Vullikanti, Mandy L Wilson, Qian Zhang. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 01.11.2017.

Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions

NASA Astrophysics Data System (ADS)

Medina, Hanoi; Tian, Di; Srivastava, Puneet; Pelosi, Anna; Chirico, Giovanni B.

2018-07-01

Reference evapotranspiration (ET0) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET0 forecasts. Impact of individual forecast variables on ET0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET0 forecast performances.

Contrasting responses to a climate regime change by sympatric, ice-dependent predators.

PubMed

Younger, Jane L; van den Hoff, John; Wienecke, Barbara; Hindell, Mark; Miller, Karen J

2016-03-15

Models that predict changes in the abundance and distribution of fauna under future climate change scenarios often assume that ecological niche and habitat availability are the major determinants of species' responses to climate change. However, individual species may have very different capacities to adapt to environmental change, as determined by intrinsic factors such as their dispersal ability, genetic diversity, generation time and rate of evolution. These intrinsic factors are usually excluded from forecasts of species' abundance and distribution changes. We aimed to determine the importance of these factors by comparing the impact of the most recent climate regime change, the late Pleistocene glacial-interglacial transition, on two sympatric, ice-dependent meso-predators, the emperor penguin (Aptenodytes forsteri) and Weddell seal (Leptonychotes weddellii). We reconstructed the population trend of emperor penguins and Weddell seals in East Antarctica over the past 75,000 years using mitochondrial DNA sequences and an extended Bayesian skyline plot method. We also assessed patterns of contemporary population structure and genetic diversity. Despite their overlapping distributions and shared dependence on sea ice, our genetic data revealed very different responses to climate warming between these species. The emperor penguin population grew rapidly following the glacial-interglacial transition, but the size of the Weddell seal population did not change. The expansion of emperor penguin numbers during the warm Holocene may have been facilitated by their higher dispersal ability and gene flow among colonies, and fine-scale differences in preferred foraging locations. The vastly different climate change responses of two sympatric ice-dependent predators suggests that differing adaptive capacities and/or fine-scale niche differences can play a major role in species' climate change responses, and that adaptive capacity should be considered alongside niche and distribution in future species forecasts.

Uses and Applications of Climate Forecasts for Power Utilities.

NASA Astrophysics Data System (ADS)

Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David

1995-05-01

The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector.

Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

2017-08-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

An overview of health forecasting.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D

2013-01-01

Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States.

PubMed

Yamana, Teresa K; Kandula, Sasikiran; Shaman, Jeffrey

2017-11-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2017-01-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time. PMID:29107987

Integration of Airborne Aerosol Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Sprigg, William A.; Huete, Alfredo; Pejanovic, Goran; Nickovic, Slobodan; Krapfl, Heide; Budge, Amy; Zelicoff, Alan; VandeWater, Peter K.; Levetin, Estelle;

Seasat data applications in ocean industries

NASA Technical Reports Server (NTRS)

Montgomery, D. R.

1985-01-01

It is pointed out that the world population expansion and resulting shortages of food, minerals, and fuel have focused additional attention on the world's oceans. In this context, aspects of weather prediction and the monitoring/prediction of long-range climatic anomalies become more important. In spite of technological advances, the commercial ocean industry and the naval forces suffer now from inadequate data and forecast products related to the oceans. The Seasat Program and the planned Navy-Remote Oceanographic Satellite System (N-ROSS) represent major contributions to improved observational coverage and the processing needed to achieve better forecasts. The Seasat Program was initiated to evaluate the effectiveness of the remote sensing of oceanographic phenomena from a satellite platform. Possible oceanographic satellite applications are presented in a table, and the impact of Seasat data on industry sectors is discussed. Attention is given to offshore oil development, deep-ocean mining, fishing, and marine transportation.

Better way to measure ageing in East Asia that takes life expectancy into account.

PubMed

Scherbov, Sergei; Sanderson, Warren C; Gietel-Basten, Stuart

2016-06-01

The aim of the study was to improve the measurement of ageing taking into account characteristics of populations and in particular changes in life expectancy. Using projected life tables, we calculated prospective old age dependency ratios (POADRs) to 2060, placing the boundary to old age at a moving point with a fixed remaining life expectancy (RLE) for all countries of East Asia. POADRs grow less rapidly than old age dependency ratios (OADRs). For example, in the Republic of Korea, the OADR is forecast to increase from around 0.1 in 1980 to around 0.8 in 2060, while the POADR is forecast to increase from around 0.1 to 0.4 over the same period. Policy makers may wish to take into account the fact that the increases in measures of ageing will be slower when those measures are adjusted for changes in life expectancy. © 2016 AJA Inc.

Changing Arctic Ecosystems: Updated forecast: Reducing carbon dioxide (CO2) emissions required to improve polar bear outlook

USGS Publications Warehouse

Oakley, Karen L.; Atwood, Todd C.; Mugel, Douglas N.; Rode, Karyn D.; Whalen, Mary E.

2015-01-01

The Arctic is warming faster than other regions of the world due to the loss of snow and ice, which increases the amount of solar energy absorbed by the region. The most visible consequence has been the rapid decline in sea ice over the last 3 decades-a decline projected to bring long ice-free summers if greenhouse gas (GHG) emissions are not significantly reduced. The polar bear (Ursus maritimus) depends on sea ice over the biologically productive continental shelves of the Arctic Ocean as a platform for hunting seals. In 2008, the U.S. Fish and Wildlife Service listed the polar bear as threatened under the Endangered Species Act (ESA) due to the threat posed by sea ice loss. The polar bear was the first species to be listed due to forecasted population declines from climate change.

Modeling Influenza Transmission Using Environmental Parameters

NASA Technical Reports Server (NTRS)

Soebiyanto, Radina P.; Kiang, Richard K.

2010-01-01

Influenza is an acute viral respiratory disease that has significant mortality, morbidity and economic burden worldwide. It infects approximately 5-15% of the world population, and causes 250,000 500,000 deaths each year. The role of environments on influenza is often drawn upon the latitude variability of influenza seasonality pattern. In regions with temperate climate, influenza epidemics exhibit clear seasonal pattern that peak during winter months, but it is not as evident in the tropics. Toward this end, we developed mathematical model and forecasting capabilities for influenza in regions characterized by warm climate Hong Kong (China) and Maricopa County (Arizona, USA). The best model for Hong Kong uses Land Surface Temperature (LST), precipitation and relative humidity as its covariates. Whereas for Maricopa County, we found that weekly influenza cases can be best modelled using mean air temperature as its covariates. Our forecasts can further guides public health organizations in targeting influenza prevention and control measures such as vaccination.

Exploring Options for an Integrated Water Level Observation Network in Alaska

NASA Astrophysics Data System (ADS)

McCammon, M.

2016-02-01

Portions' of Alaska's remote coastlines are among the Nation's most vulnerable to geohazards such as tsunami, extra-tropical storm surge, and erosion; and the availability of observations of water levels, ocean waves, and river discharge are severely lacking to support water level warnings and forecasts. Alaska is experiencing dramatic reductions in sea ice cover, changes in extra-tropical storm surge patterns, and thawing permafrost. These conditions are endangering coastal populations throughout the State. Gaps in the ocean observing system limit our State's ability to provide useful marine and sea ice forecasts, especially in the Arctic. A spectrum of observation platforms may provide an optimal solution for filling the most critical gaps in these coastal and ocean areas. The collaborations described in this talk and better leveraging of resources and capabilities across federal, state, and academic partners will provide the best opportunity for advancing our science capacity and capabilities in this remote region.

Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts - A Hydrologic Model Output Statistics (HMOS) approach

NASA Astrophysics Data System (ADS)

Regonda, Satish Kumar; Seo, Dong-Jun; Lawrence, Bill; Brown, James D.; Demargne, Julie

2013-08-01

We present a statistical procedure for generating short-term ensemble streamflow forecasts from single-valued, or deterministic, streamflow forecasts produced operationally by the U.S. National Weather Service (NWS) River Forecast Centers (RFCs). The resulting ensemble streamflow forecast provides an estimate of the predictive uncertainty associated with the single-valued forecast to support risk-based decision making by the forecasters and by the users of the forecast products, such as emergency managers. Forced by single-valued quantitative precipitation and temperature forecasts (QPF, QTF), the single-valued streamflow forecasts are produced at a 6-h time step nominally out to 5 days into the future. The single-valued streamflow forecasts reflect various run-time modifications, or "manual data assimilation", applied by the human forecasters in an attempt to reduce error from various sources in the end-to-end forecast process. The proposed procedure generates ensemble traces of streamflow from a parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecast, QPF, and the most recent streamflow observation. For parameter estimation and evaluation, we used a multiyear archive of the single-valued river stage forecast produced operationally by the NWS Arkansas-Red River Basin River Forecast Center (ABRFC) in Tulsa, Oklahoma. As a by-product of parameter estimation, the procedure provides a categorical assessment of the effective lead time of the operational hydrologic forecasts for different QPF and forecast flow conditions. To evaluate the procedure, we carried out hindcasting experiments in dependent and cross-validation modes. The results indicate that the short-term streamflow ensemble hindcasts generated from the procedure are generally reliable within the effective lead time of the single-valued forecasts and well capture the skill of the single-valued forecasts. For smaller basins, however, the effective lead time is significantly reduced by short basin memory and reduced skill in the single-valued QPF.

U.S. High Seas Marine Text Forecasts by Area

Science.gov Websites

Flooding Tsunamis 406 EPIRB's U.S. High Seas Marine Text Forecasts by Area OPC N.Atlantic High Seas Forecast NHC N.Atlantic High Seas Forecast OPC N.Pacific High Seas Forecast HFO N.Pacific High Seas Forecast NHC N.Pacific High Seas Forecast HFO S.Pacific High Seas Forecast U.S. High Seas Marine Text

A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

NASA Astrophysics Data System (ADS)

Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

2017-12-01

The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches

NASA Astrophysics Data System (ADS)

Omi, Takahiro; Ogata, Yosihiko; Hirata, Yoshito; Aihara, Kazuyuki

2015-04-01

Because aftershock occurrences can cause significant seismic risks for a considerable time after the main shock, prospective forecasting of the intermediate-term aftershock activity as soon as possible is important. The epidemic-type aftershock sequence (ETAS) model with the maximum likelihood estimate effectively reproduces general aftershock activity including secondary or higher-order aftershocks and can be employed for the forecasting. However, because we cannot always expect the accurate parameter estimation from incomplete early aftershock data where many events are missing, such forecasting using only a single estimated parameter set (plug-in forecasting) can frequently perform poorly. Therefore, we here propose Bayesian forecasting that combines the forecasts by the ETAS model with various probable parameter sets given the data. By conducting forecasting tests of 1 month period aftershocks based on the first 1 day data after the main shock as an example of the early intermediate-term forecasting, we show that the Bayesian forecasting performs better than the plug-in forecasting on average in terms of the log-likelihood score. Furthermore, to improve forecasting of large aftershocks, we apply a nonparametric (NP) model using magnitude data during the learning period and compare its forecasting performance with that of the Gutenberg-Richter (G-R) formula. We show that the NP forecast performs better than the G-R formula in some cases but worse in other cases. Therefore, robust forecasting can be obtained by employing an ensemble forecast that combines the two complementary forecasts. Our proposed method is useful for a stable unbiased intermediate-term assessment of aftershock probabilities.

A study on the epidemiological characteristics and infectious forecast model of malaria at Guangzhou Airport among Chinese returnees from Africa.

PubMed

Wu, Hui-Ming; Fang, Zhi-Qiang; Zhao, Dang; Chen, Yan-Ling; Liu, Chuan-Ge; Liang, Xi

2017-07-04

Cross-border malaria transmission in China is a major component of Chinese imported malaria cases. Such cases mostly are travellers returning from malaria endemic countries in Africa. By investigating malaria infectious status among Chinese worker in Africa, this study analysed the malaria risk factors, in order to establish infectious forecast model. Chinese returnees data from Africa were collected at Guangzhou Baiyun International Airport, Guangzhou, China between August 2015 and March 2016 and were included in the cross-sectional and retrospective survey. A total of 1492 respondents were included in the study with the majority consisting of junior middle school educated male. Most of them are manual and technical workers hired by companies, with average of 37.04 years of age. Overall malaria incidence rate of the population was 8.98% (134/1492), and there were no significant differences regarding age, gender, occupation, or team. Forecast model was developed on the basis of malaria risk factors including working country, local ecological environment type, work duration and intensity of mosquito bite prevention. The survey suggested that malaria incidence was high among Chinese travellers who had worked in Africa countries of heavy malaria burden. Further research on the frequency and severity of clinical episodes among Chinese travellers having worked in Africa is needed.

Diversity modelling for electrical power system simulation

NASA Astrophysics Data System (ADS)

Sharip, R. M.; Abu Zarim, M. A. U. A.

2013-12-01

This paper considers diversity of generation and demand profiles against the different future energy scenarios and evaluates these on a technical basis. Compared to previous studies, this research applied a forecasting concept based on possible growth rates from publically electrical distribution scenarios concerning the UK. These scenarios were created by different bodies considering aspects such as environment, policy, regulation, economic and technical. In line with these scenarios, forecasting is on a long term timescale (up to every ten years from 2020 until 2050) in order to create a possible output of generation mix and demand profiles to be used as an appropriate boundary condition for the network simulation. The network considered is a segment of rural LV populated with a mixture of different housing types. The profiles for the 'future' energy and demand have been successfully modelled by applying a forecasting method. The network results under these profiles shows for the cases studied that even though the value of the power produced from each Micro-generation is often in line with the demand requirements of an individual dwelling there will be no problems arising from high penetration of Micro-generation and demand side management for each dwellings considered. The results obtained highlight the technical issues/changes for energy delivery and management to rural customers under the future energy scenarios.

Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks

PubMed Central

Thompson, Robin N.; Gilligan, Christopher A.; Cunniffe, Nik J.

2016-01-01

We assess how presymptomatic infection affects predictability of infectious disease epidemics. We focus on whether or not a major outbreak (i.e. an epidemic that will go on to infect a large number of individuals) can be predicted reliably soon after initial cases of disease have appeared within a population. For emerging epidemics, significant time and effort is spent recording symptomatic cases. Scientific attention has often focused on improving statistical methodologies to estimate disease transmission parameters from these data. Here we show that, even if symptomatic cases are recorded perfectly, and disease spread parameters are estimated exactly, it is impossible to estimate the probability of a major outbreak without ambiguity. Our results therefore provide an upper bound on the accuracy of forecasts of major outbreaks that are constructed using data on symptomatic cases alone. Accurate prediction of whether or not an epidemic will occur requires records of symptomatic individuals to be supplemented with data concerning the true infection status of apparently uninfected individuals. To forecast likely future behavior in the earliest stages of an emerging outbreak, it is therefore vital to develop and deploy accurate diagnostic tests that can determine whether asymptomatic individuals are actually uninfected, or instead are infected but just do not yet show detectable symptoms. PMID:27046030

Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting.

PubMed

Jahanishakib, Fatemeh; Mirkarimi, Seyed Hamed; Salmanmahiny, Abdolrassoul; Poodat, Fatemeh

2018-05-08

Efficient land use management requires awareness of past changes, present actions, and plans for future developments. Part of these requirements is achieved using scenarios that describe a future situation and the course of changes. This research aims to link scenario results with spatially explicit and quantitative forecasting of land use development. To develop land use scenarios, SMIC PROB-EXPERT and MORPHOL methods were used. It revealed eight scenarios as the most probable. To apply the scenarios, we considered population growth rate and used a cellular automata-Markov chain (CA-MC) model to implement the quantified changes described by each scenario. For each scenario, a set of landscape metrics was used to assess the ecological integrity of land use classes in terms of fragmentation and structural connectivity. The approach enabled us to develop spatial scenarios of land use change and detect their differences for choosing the most integrated landscape pattern in terms of landscape metrics. Finally, the comparison between paired forecasted scenarios based on landscape metrics indicates that scenarios 1-1, 2-2, 3-2, and 4-1 have a more suitable integrity. The proposed methodology for developing spatial scenarios helps executive managers to create scenarios with many repetitions and customize spatial patterns in real world applications and policies.

Developing a user-friendly Drought Monitoring and Forecasting Tool for Doctors without Borders

NASA Astrophysics Data System (ADS)

Enenkel, Markus

2015-04-01

Humanitarian aid organizations that focus on drought-related emergency response and disaster preparedness need to take decisions under high uncertainty. Satellite-derived and modelled information can help to decrease this uncertainty. However, in order to benefit from the provided knowledge it is crucial to adapt datasets and tools to actual user requirements and existing organizational capacities. Furthermore, socio-economic vulnerabilities (e. g. current rates of malnutrition) and coping capacities (e. g. access to drought-resistant seeds) of the affected population need to be assessed to link environmental conditions (drought risk) to potential impacts (food insecurity). Forecasts with lead times up to several months are desirable from a logistic point of view, but naturally less accurate than short-term predictions. As a consequence, careful calibration is required to identify and balance forecasts with an acceptable accuracy and the risk of possible false alarms. Therefore, we calibrate modelled predictions of rainfall, temperature and soil moisture via satellite-derived observations. Field tests with Doctors without Borders in Ethiopia help to define critical thresholds, to interpret the information under real conditions and to collect the necessary additional socio-economic data via a smartphone app. The final risk maps need to be visualized in a way that is easy to interpret, but not oversimplified.

[A study on dental manpower distribution in Shanghai Pudong new district].

PubMed

Gu, Qin; Feng, Xi-ping

2006-02-01

A study of dental manpower distribution was made in Shanghai Pudong new district in order to analyze the needs and demands for dental services in Shanghai Pudong new district, to forecast the developmental trends of dental demand in the future and to provide basis for regional programs of dental manpower in the urban areas of China. An analysis was made in 601 subjects taken from all age groups in Shanghai Pudong new district by stratified and cluster random sampling and in 83 medical institutions of stomatology in Shanghai Pudong new district by mass examination. The amount of dental manpower need and demand was computed and forecasted by means of health care need and demand and proportional analogy methods. The total amounts needed were 755-834 dentists. The total amounts demanded were 285-314 dentists. It was forecasted that the figures would be 392-1041 in the year of 2010. The prevalence of oral disease was 90.18%, but only 37.66% of subjects visited dentist in a year. The ratio of dentists to the population was 1:9375. The unbalance between demand for and supply of dental manpower was mainly due to negative awareness of people, the irrationalness of demand levels, problems from service provider and the irrationalness of dental manpower levels.

Stability of strong species interactions resist the synergistic effects of local and global pollution in kelp forests.

PubMed

Falkenberg, Laura J; Russell, Bayden D; Connell, Sean D

2012-01-01

Foundation species, such as kelp, exert disproportionately strong community effects and persist, in part, by dominating taxa that inhibit their regeneration. Human activities which benefit their competitors, however, may reduce stability of communities, increasing the probability of phase-shifts. We tested whether a foundation species (kelp) would continue to inhibit a key competitor (turf-forming algae) under moderately increased local (nutrient) and near-future forecasted global pollution (CO(2)). Our results reveal that in the absence of kelp, local and global pollutants combined to cause the greatest cover and mass of turfs, a synergistic response whereby turfs increased more than would be predicted by adding the independent effects of treatments (kelp absence, elevated nutrients, forecasted CO(2)). The positive effects of nutrient and CO(2) enrichment on turfs were, however, inhibited by the presence of kelp, indicating the competitive effect of kelp was stronger than synergistic effects of moderate enrichment of local and global pollutants. Quantification of physicochemical parameters within experimental mesocosms suggests turf inhibition was likely due to an effect of kelp on physical (i.e. shading) rather than chemical conditions. Such results indicate that while forecasted climates may increase the probability of phase-shifts, maintenance of intact populations of foundation species could enable the continued strength of interactions and persistence of communities.

A High Resolution Tropical Cyclone Power Outage Forecasting Model for the Continental United States

NASA Astrophysics Data System (ADS)

Pino, J. V.; Quiring, S. M.; Guikema, S.; Shashaani, S.; Linger, S.; Backhaus, S.

2017-12-01

Tropical cyclones cause extensive damage to the power infrastructure system throughout the United States. This damage can leave millions without power for extended periods of time, as most recently seen with Hurricane Matthew (2016). Accurate and timely prediction of power outages are essential for utility companies, emergency management agencies, and governmental organizations. Here we present a high-resolution (250 m x 250 m) hurricane power outage model for the United States. The model uses only publicly-available data to make predictions. It uses forecasts of storm variables such as maximum 3-second wind gust, duration of strong winds > 20 m s-2, soil moisture, and precipitation. It also incorporates static environmental variables such as elevation characteristics, land cover type, population density, tree species data, and root zone depth. A web tool was established for use by the Department of Energy (DOE) so that the model can be used for real-time outage forecasting or for synthetic tropical cyclones as an exercise in emergency management. This web tool provides DOE decision-makers with high impact analytic results and products that can be disseminated to federal, local, and state agencies. The results then aid utility companies in their pre- and post-storm activities, thus decreasing restoration times and lowering costs.

Quantifying model uncertainty in seasonal Arctic sea-ice forecasts

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, Edward; Barthélemy, Antoine; Chevallier, Matthieu; Cullather, Richard; Fučkar, Neven; Massonnet, François; Posey, Pamela; Wang, Wanqiu; Zhang, Jinlun; Ardilouze, Constantin; Bitz, Cecilia; Vernieres, Guillaume; Wallcraft, Alan; Wang, Muyin

2017-04-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or post-processing techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

Model-free aftershock forecasts constructed from similar sequences in the past

NASA Astrophysics Data System (ADS)

van der Elst, N.; Page, M. T.

2017-12-01

The basic premise behind aftershock forecasting is that sequences in the future will be similar to those in the past. Forecast models typically use empirically tuned parametric distributions to approximate past sequences, and project those distributions into the future to make a forecast. While parametric models do a good job of describing average outcomes, they are not explicitly designed to capture the full range of variability between sequences, and can suffer from over-tuning of the parameters. In particular, parametric forecasts may produce a high rate of "surprises" - sequences that land outside the forecast range. Here we present a non-parametric forecast method that cuts out the parametric "middleman" between training data and forecast. The method is based on finding past sequences that are similar to the target sequence, and evaluating their outcomes. We quantify similarity as the Poisson probability that the observed event count in a past sequence reflects the same underlying intensity as the observed event count in the target sequence. Event counts are defined in terms of differential magnitude relative to the mainshock. The forecast is then constructed from the distribution of past sequences outcomes, weighted by their similarity. We compare the similarity forecast with the Reasenberg and Jones (RJ95) method, for a set of 2807 global aftershock sequences of M≥6 mainshocks. We implement a sequence-specific RJ95 forecast using a global average prior and Bayesian updating, but do not propagate epistemic uncertainty. The RJ95 forecast is somewhat more precise than the similarity forecast: 90% of observed sequences fall within a factor of two of the median RJ95 forecast value, whereas the fraction is 85% for the similarity forecast. However, the surprise rate is much higher for the RJ95 forecast; 10% of observed sequences fall in the upper 2.5% of the (Poissonian) forecast range. The surprise rate is less than 3% for the similarity forecast. The similarity forecast may be useful to emergency managers and non-specialists when confidence or expertise in parametric forecasting may be lacking. The method makes over-tuning impossible, and minimizes the rate of surprises. At the least, this forecast constitutes a useful benchmark for more precisely tuned parametric forecasts.

A study for systematic errors of the GLA forecast model in tropical regions

NASA Technical Reports Server (NTRS)

Chen, Tsing-Chang; Baker, Wayman E.; Pfaendtner, James; Corrigan, Martin

1988-01-01

From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system.

Multi-Year Revenue and Expenditure Forecasting for Small Municipal Governments.

DTIC Science & Technology

1981-03-01

Management Audit Econometric Revenue Forecast Gap and Impact Analysis Deterministic Expenditure Forecast Municipal Forecasting Municipal Budget Formlto...together with a multi-year revenue and expenditure forecasting model for the City of Monterey, California. The Monterey model includes an econometric ...65 5 D. FORECAST BASED ON THE ECONOMETRIC MODEL ------- 67 E. FORECAST BASED ON EXPERT JUDGMENT AND TREND ANALYSIS

Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

NASA Technical Reports Server (NTRS)

Wang, Yao; Sridhar, Banavar

2010-01-01

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.

A watershed-based spatially-explicit demonstration of an integrated environmental modeling framework for ecosystem services in the Coal River Basin (WV, USA)

Treesearch

John M. Johnston; Mahion C. Barber; Kurt Wolfe; Mike Galvin; Mike Cyterski; Rajbir Parmar; Luis Suarez

2016-01-01

We demonstrate a spatially-explicit regional assessment of current condition of aquatic ecoservices in the Coal River Basin (CRB), with limited sensitivity analysis for the atmospheric contaminant mercury. The integrated modeling framework (IMF) forecasts water quality and quantity, habitat suitability for aquatic biota, fish biomasses, population densities, ...

Forecasting the Relative and Cumulative Effects of Multiple Stressors on At-risk Populations

DTIC Science & Technology

2011-08-01

Vitals (observed vital rates), Movement, Ranges, Barriers (barrier interactions), Stochasticity (a time series of stochasticity indices...Simulation Viewer are themselves stochastic . They can change each time it is run. B. 196 Analysis If multiple Census events are present in the life...30-year period. A monthly time series was generated for the 20th-century using monthly anomalies for temperature, precipitation, and percent

Merging of the Recruitment Branch and the Political Information Branch of the German Armed Forces and Application of this Recruitment Network to Saudi Arabia

DTIC Science & Technology

2013-03-01

18 a. Reduce Expenditures ................................................. 18 b. Increase Target Group Contacts...84 x THIS PAGE INTENTIONALLY LEFT BLANK xi LIST OF TABLES Table 1. Target Group and Population Forecast for...forces. Both branches are kept separate from each other, but focus on the same target group (young people between 16 and 24 years of age). While

A Conceptual Logistic System for Iranian Enterprise

DTIC Science & Technology

1980-06-01

forecasting techniques that can be applied within the organization according to the situation and environment that the company confronts. It must be...determining quan- tities to be purchased. Of course, it must be understood that all of these factors do not apply equally to all companies , nor indeed...separated geographically Production companies are not looking for the most populated area and consumers are not willing to live close to the production

Global Forecasts of Urban Expansion to 2030 and Direct Impacts on Biodiversity and Carbon Pools

NASA Astrophysics Data System (ADS)

Seto, K. C.; Guneralp, B.; Hutyra, L.

2012-12-01

Urban land cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. Yet, despite projections that world urban populations will increase to 4.3 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop the first global probabilistic forecasts of urban land cover change and explore the impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue, then by 2030, urban land cover will expand between 800,000 and 3.3 million km2, representing a doubling to five-fold increase from the global urban land cover in 2000. This would result in considerable loss of habitats in key biodiversity hotspots, including the Guinean forests of West Africa, Tropical Andes, Western Ghats and Sri Lanka. Within the pan-tropics, loss in forest biomass from urban expansion is estimated to be 1.38 PgC (0.05 PgC yr-1), equal to approximately 5% of emissions from tropical land use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and forest carbon losses.

New England Cod Collapse and the Climate.

PubMed

Meng, Kyle C; Oremus, Kimberly L; Gaines, Steven D

2016-01-01

To improve fishery management, there is an increasing need to understand the long-term consequences of natural and anthropogenic climate variability for ecological systems. New England's iconic cod populations have been in decline for several decades and have recently reached unprecedented lows. We find that 17% of the overall decline in Gulf of Maine cod biomass since 1980 can be attributed to positive phases of the North Atlantic Oscillation (NAO). This is a consequence of three results: i) a 1-unit increase in the NAO winter index is associated with a 17% decrease in the spring biomass of age-1 cod the following year; ii) this NAO-driven decrease persists as the affected cohort matures; iii) fishing practices appear to exacerbate NAO's direct biological effect such that, since 1913, a 1-unit increase in the NAO index lowers subsequent cod catch for up to 19 years. The Georges Bank cod stock displays similar patterns. Because we statistically detect a delay between the NAO and subsequent declines in adult biomass, our findings imply that observed current NAO conditions can be used in stock forecasts, providing lead time for adaptive policy. More broadly, our approach can inform forecasting efforts for other fish populations strongly affected by natural and anthropogenic climatic variation.

New England Cod Collapse and the Climate

PubMed Central

Meng, Kyle C.; Oremus, Kimberly L.; Gaines, Steven D.

2016-01-01

To improve fishery management, there is an increasing need to understand the long-term consequences of natural and anthropogenic climate variability for ecological systems. New England’s iconic cod populations have been in decline for several decades and have recently reached unprecedented lows. We find that 17% of the overall decline in Gulf of Maine cod biomass since 1980 can be attributed to positive phases of the North Atlantic Oscillation (NAO). This is a consequence of three results: i) a 1-unit increase in the NAO winter index is associated with a 17% decrease in the spring biomass of age-1 cod the following year; ii) this NAO-driven decrease persists as the affected cohort matures; iii) fishing practices appear to exacerbate NAO’s direct biological effect such that, since 1913, a 1-unit increase in the NAO index lowers subsequent cod catch for up to 19 years. The Georges Bank cod stock displays similar patterns. Because we statistically detect a delay between the NAO and subsequent declines in adult biomass, our findings imply that observed current NAO conditions can be used in stock forecasts, providing lead time for adaptive policy. More broadly, our approach can inform forecasting efforts for other fish populations strongly affected by natural and anthropogenic climatic variation. PMID:27463967

Bayesian Modeling of Prion Disease Dynamics in Mule Deer Using Population Monitoring and Capture-Recapture Data

PubMed Central

Geremia, Chris; Miller, Michael W.; Hoeting, Jennifer A.; Antolin, Michael F.; Hobbs, N. Thompson

2015-01-01

Epidemics of chronic wasting disease (CWD) of North American Cervidae have potential to harm ecosystems and economies. We studied a migratory population of mule deer (Odocoileus hemionus) affected by CWD for at least three decades using a Bayesian framework to integrate matrix population and disease models with long-term monitoring data and detailed process-level studies. We hypothesized CWD prevalence would be stable or increase between two observation periods during the late 1990s and after 2010, with higher CWD prevalence making deer population decline more likely. The weight of evidence suggested a reduction in the CWD outbreak over time, perhaps in response to intervening harvest-mediated population reductions. Disease effects on deer population growth under current conditions were subtle with a 72% chance that CWD depressed population growth. With CWD, we forecasted a growth rate near one and largely stable deer population. Disease effects appear to be moderated by timing of infection, prolonged disease course, and locally variable infection. Long-term outcomes will depend heavily on whether current conditions hold and high prevalence remains a localized phenomenon. PMID:26509806

A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

NASA Astrophysics Data System (ADS)

Schepen, Andrew; Zhao, Tongtiegang; Wang, Quan J.; Robertson, David E.

2018-03-01

Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

Planning documents: a business planning strategy.

PubMed

Kaehrle, P A

2000-06-01

Strategic planning and business plan development are essential nursing management skills in today's competitive, fast paced, continually changing health care environment. Even in times of great uncertainty, nurse managers need to plan and forecast for the future. A well-written business plan allows nurse managers to communicate their expertise and proactively contribute to the programmatic decisions and changes occurring within their patient population or service area. This article presents the use of planning documents as a practical, strategic business planning strategy. Although the model addresses orthopedic services specifically, nurse managers can gain an understanding and working knowledge of planning concepts that can be applied to all patient populations.

Bayesian analyses of seasonal runoff forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, R.; Reese, S.

1991-12-01

Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation.

PubMed

Recknagel, Friedrich; Orr, Philip T; Cao, Hongqing

2014-01-01

Seven-day-ahead forecasting models of Cylindrospermopsis raciborskii in three warm-monomictic and mesotrophic reservoirs in south-east Queensland have been developed by means of water quality data from 1999 to 2010 and the hybrid evolutionary algorithm HEA. Resulting models using all measured variables as inputs as well as models using electronically measurable variables only as inputs forecasted accurately timing of overgrowth of C. raciborskii and matched well high and low magnitudes of observed bloom events with 0.45≤r 2 >0.61 and 0.4≤r 2 >0.57, respectively. The models also revealed relationships and thresholds triggering bloom events that provide valuable information on synergism between water quality conditions and population dynamics of C. raciborskii. Best performing models based on using all measured variables as inputs indicated electrical conductivity (EC) within the range of 206-280mSm -1 as threshold above which fast growth and high abundances of C. raciborskii have been observed for the three lakes. Best models based on electronically measurable variables for the Lakes Wivenhoe and Somerset indicated a water temperature (WT) range of 25.5-32.7°C within which fast growth and high abundances of C. raciborskii can be expected. By contrast the model for Lake Samsonvale highlighted a turbidity (TURB) level of 4.8 NTU as indicator for mass developments of C. raciborskii. Experiments with online measured water quality data of the Lake Wivenhoe from 2007 to 2010 resulted in predictive models with 0.61≤r 2 >0.65 whereby again similar levels of EC and WT have been discovered as thresholds for outgrowth of C. raciborskii. The highest validity of r 2 =0.75 for an in situ data-based model has been achieved after considering time lags for EC by 7 days and dissolved oxygen by 1 day. These time lags have been discovered by a systematic screening of all possible combinations of time lags between 0 and 10 days for all electronically measurable variables. The so-developed model performs seven-day-ahead forecasts and is currently implemented and tested for early warning of C. raciborskii blooms in the Wivenhoe reservoir. Copyright © 2013 Elsevier B.V. All rights reserved.

Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: A case study in endemic districts of Bhutan

PubMed Central

2010-01-01

Background Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX. Methods This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month. Results It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and (1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010, where population in 2009 was 285,375 and the expected population of 2010 to be 289,085. The ARIMAX model of monthly cases and climatic factors showed considerable variations among the different districts. In general, the mean maximum temperature lagged at one month was a strong positive predictor of an increased malaria cases for four districts. The monthly number of cases of the previous month was also a significant predictor in one district, whereas no variable could predict malaria cases for two districts. Conclusions The ARIMA models of time-series analysis were useful in forecasting the number of cases in the endemic areas of Bhutan. There was no consistency in the predictors of malaria cases when using ARIMAX model with selected lag times and climatic predictors. The ARIMA forecasting models could be employed for planning and managing malaria prevention and control programme in Bhutan. PMID:20813066

Communicating uncertainty in hydrological forecasts: mission impossible?

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian

2010-05-01

Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted scenarios, is essential. We believe that the efficient communication of uncertainty in hydro-meteorological forecasts is not a mission impossible. Questions remaining unanswered in probabilistic hydrological forecasting should not neutralize the goal of such a mission, and the suspense kept should instead act as a catalyst for overcoming the remaining challenges.

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

NASA Astrophysics Data System (ADS)

Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.

2015-12-01

The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help MOSWOC forecasters view verification results in near real-time; plans to objectively assess flare forecasts under the EU Horizon 2020 FLARECAST project; and summarise ISES efforts to achieve consensus on verification.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

Forecasting Consumer Adoption of Information Technology and Services--Lessons from Home Video Forecasting.

ERIC Educational Resources Information Center

Klopfenstein, Bruce C.

1989-01-01

Describes research that examined the strengths and weaknesses of technological forecasting methods by analyzing forecasting studies made for home video players. The discussion covers assessments and explications of correct and incorrect forecasting assumptions, and their implications for forecasting the adoption of home information technologies…

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

NASA Astrophysics Data System (ADS)

Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

2017-09-01

Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

EU pharmaceutical expenditure forecast.

PubMed

Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States' pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012-2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (-€9,367 million), France (-€5,589 million), and, far behind them, Germany (-€831 million), Greece (-€808 million), Portugal (-€243 million), and Hungary (-€84 million). The main source of savings came from the cardiovascular, central nervous system, and respiratory areas and from biosimilar entries. Oncology, immunology, and inflammation, in contrast, lead to additional expenditure. The model was particularly sensitive to the time to market of branded products, generic prices, generic penetration, and the distribution of biosimilars. The results of this forecast suggested a decrease in pharmaceutical expenditure in the studied period. The model was sensitive to pharmaceutical policy decisions.