Jennifer Small

Prediction of a Rift Valley fever outbreak

El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outbreak.

Satellite remote sensing of primary production : an improved production efficiency modeling approach

Application and validation of a modified production efficiency model (PEM) appropriate for the regional and global scales is presented. The model calculates not just the conversion efficiency of absorbed photosynthetically active radiation (APAR) but also the component carbon fluxes that ultimately determine net and gross primary production. This approach, driven with remotely sensed observations, moves beyond simple correlative or associative models to a more mechanistic basis and avoids the need for a full suite of ecophysiological process algorithms that require explicit (e.g. species-specific) parameterization. We show that surface variables recovered from the satellite observations, including net primary production, are in good agreement with field measurements and independent model simulations in a number of ecosystems. These results illustrate the utility of PEMs for terrestrial primary production modeling over large areas and suggest that some complex ecophysiological models may be functionally simpler than their structure suggests.

Developing global climate anomalies suggest potential disease risks for 2006 – 2007

BackgroundEl Niño/Southern Oscillation (ENSO) related climate anomalies have been shown to have an impact on infectious disease outbreaks. The Climate Prediction Center of the National Oceanic and Atmospheric Administration (NOAA/CPC) has recently issued an unscheduled El Niño advisory, indicating that warmer than normal sea surface temperatures across the equatorial eastern Pacific may have pronounced impacts on global tropical precipitation patterns extending into the northern hemisphere particularly over North America. Building evidence of the links between ENSO driven climate anomalies and infectious diseases, particularly those transmitted by insects, can allow us to provide improved long range forecasts of an epidemic or epizootic. We describe developing climate anomalies that suggest potential disease risks using satellite generated data.ResultsSea surface temperatures (SSTs) in the equatorial east Pacific ocean have anomalously increased significantly during July – October 2006 indicating the typical development of El Niño conditions. The persistence of these conditions will lead to extremes in global-scale climate anomalies as has been observed during similar conditions in the past. Positive Outgoing Longwave Radiation (OLR) anomalies, indicative of severe drought conditions, have been observed across all of Indonesia, Malaysia and most of the Philippines, which are usually the first areas to experience ENSO-related impacts. This dryness can be expected to continue, on average, for the remainder of 2006 continuing into the early part of 2007. During the period November 2006 – January 2007 climate forecasts indicate that there is a high probability for above normal rainfall in the central and eastern equatorial Pacific Islands, the Korean Peninsula, the U.S. Gulf Coast and Florida, northern South America and equatorial east Africa. Taking into consideration current observations and climate forecast information, indications are that the following regions are at increased risk for disease outbreaks: Indonesia, Malaysia, Thailand and most of the southeast Asia Islands for increased dengue fever transmission and increased respiratory illness; Coastal Peru, Ecuador, Venezuela, and Colombia for increased risk of malaria; Bangladesh and coastal India for elevated risk of cholera; East Africa for increased risk of a Rift Valley fever outbreak and elevated malaria; southwest USA for increased risk for hantavirus pulmonary syndrome and plague; southern California for increased West Nile virus transmission; and northeast Brazil for increased dengue fever and respiratory illness.ConclusionThe current development of El Niño conditions has significant implications for global public health. Extremes in climate events with above normal rainfall and flooding in some regions and extended drought periods in other regions will occur. Forecasting disease is critical for timely and efficient planning of operational control programs. In this paper we describe developing global climate anomalies that suggest potential disease risks that will give decision makers additional tools to make rational judgments concerning implementation of disease prevention and mitigation strategies.

Remotely Sensed Interannual Variations and Trends in Terrestrial Net Primary Productivity 1981–2000

Spatial and temporal variations in net primary production (NPP) are of great importance to ecological studies, natural resource management, and terrestrial carbon sink estimates. However, most of the existing estimates of interannual variation in NPP at regional and global scales were made at coarse resolutions with climate-driven process models. In this study, we quantified global NPP variation at an 8 km and 10-day resolution from 1981 to 2000 based on satellite observations. The high resolution was achieved using the GLObal Production Efficiency Model (GLO-PEM), which was driven with variables derived almost entirely from satellite remote sensing. The results show that there was an increasing trend toward enhanced terrestrial NPP that was superimposed on high seasonal and interannual variations associated with climate variability and that the increase was occurring in both northern and tropical latitudes. NPP generally decreased in El Niño season and increased in La Niña seasons, but the magnitude and spatial pattern of the response varied widely between individual events. Our estimates also indicate that the increases in NPP during the period were caused mainly by increases in atmospheric carbon dioxide and precipitation. The enhancement of NPP by warming was limited to northern high latitudes (above 50°N); in other regions, the interannual variations in NPP were correlated negatively with temperature and positively with precipitation.

Prediction, Assessment of the Rift Valley Fever Activity in East and Southern Africa 2006-2008 and Possible Vector Control Strategies

Historical outbreaks of Rift Valley fever (RVF) since the early 1950s have been associated with cyclical patterns of the El Niño/Southern Oscillation (ENSO) phenomenon, which results in elevated and widespread rainfall over the RVF endemic areas of Africa. Using satellite measurements of global and regional elevated sea surface temperatures, elevated rainfall, and satellite derived-normalized difference vegetation index data, we predicted with lead times of 2-4 months areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa, Sudan, and Southern Africa at different time periods from September 2006 to March 2008. Predictions were confirmed by entomological field investigations of virus activity and by reported cases of RVF in human and livestock populations. This represents the first series of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation into the future.

Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective global agricultural monitoring system as they offer timely, objective, global information on croplands distribution, crop development and conditions as the growing season progresses. The Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, UMD and SDSU initiative, has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) with timely, easily accessible, scientifically-validated remotely-sensed data and derived products as well as data analysis tools, for crop-condition monitoring and production assessment. This system is an integral component of the USDA’s FAS Decision Support System (DSS) for agriculture. It has significantly improved the FAS crop analysts’ ability to monitor crop conditions, and to quantitatively forecast crop yields through the provision of timely, high-quality global earth observations data in a format customized for FAS alongside a suite of data analysis tools. FAS crop analysts use these satellite data in a ‘convergence of evidence’ approach with meteorological data, field reports, crop models, attache reports and local reports. The USDA FAS is currently the only operational provider of timely, objective crop production forecasts at the global scale. These forecasts are routinely used by the other US Federal government agencies as well as by commodity trading companies, farmers, relief agencies and foreign governments. This paper discusses the operational components and new developments of the GLAM monitoring system as well as the future role of earth observations in global agricultural monitoring.

Interannual variability of global terrestrial primary production: Results of a model driven with satellite observations

Interannual variation in terrestrial net primary production (NPP) was modeled using the global production efficiency model (GLO-PEM), a semimechanistic plant photosynthesis and respiration model driven entirely with satellite advanced very high resolution radiometer (AVHRR) observations. The model also estimated a wide range of biophysical variables at 10-day intervals for the period 1982–1989, including air temperature, vapor pressure deficit, soil moisture, biomass, autotrophic respiration, canopy-absorbed photosynthetically active radiation, gross primary production, and light use efficiency. The accuracy of the simulated variables has previously been shown to be within 10–30% of field measurements, depending on the specific variable. We analyze here interannual changes in NPP, which showed large spatial variability (0–1500 gC m−2 yr−1) and trends that differed regionally over the 8-year period. Annually integrated global NPP was found to vary as much as 12% between years and was very sensitive to air temperature. The coefficient of variation in NPP of sparsely vegetated areas (mostly semiarid) on an interannual basis was as much as 80%, whereas densely vegetated areas (broadleaf evergreen and seasonally deciduous forests) varied comparatively little (0–10%). Mean annual NPP of the latter decreased 36 gC m−2 yr−1 over the time series examined. There was extreme seasonal and moderate interannual variation (10–60%) in NPP of middle- to high-latitude regions (temperate and boreal forests) with evidence for a slight trend toward increased values through time (+3 to 12 gC m−2 yr−1). The results indicate significant interannual and regional differences in responses to climate variability, with boreal regions increasing 39 gC m−2 yr−1 compared to a decrease of 116 gC m−2 yr−1 in tropical regions for each 1°C rise in air temperature. We explore a few of the possible reasons for these observations and discuss some of the issues and limitations to the use of the current global AVHRR observational record.

Climatic suitability for malaria transmission in Africa, 1911–1995

Time series analysis of a climate-driven model of malaria transmission shows limited evidence for an increase in suitability during the last century across Africa. Outside areas where climate was always or never suitable, <17% of the continent showed significant trends in malaria transmission. Of these areas, 5.7% showed positive deterministic trends, 6.1% had negative deterministic trends, and 5.1% exhibited stochastic trends. In areas with positive trends, precipitation, rather than temperature, was the primary forcing variable. This analysis highlights the need to examine the relationship between climate and malaria more closely and to fully consider nonclimatic factors as drivers of increased malaria transmission across the continent.

Advances in satellite remote sensing of environmental variables for epidemiological applications

Earth-observing satellites have provided an unprecedented view of the land surface but have been exploited relatively little for the measurement of environmental variables of particular relevance to epidemiology. Recent advances in techniques to recover continuous fields of air temperature, humidity, and vapour pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications. We report on the development of techniques to map environmental variables with relevance to the prediction of the relative abundance of disease vectors and intermediate hosts. Improvements to current methods of obtaining information on vegetation properties, canopy and surface temperature and soil moisture over large areas are also discussed. Algorithms used to measure these variables incorporate visible, near-infrared and thermal infrared radiation observations derived from time series of satellite-based sensors, focused here primarily but not exclusively on the Advanced Very High Resolution Radiometer (AVHRR) instruments. The variables compare favourably with surface measurements over a broad array of conditions at several study sites, and maps of retrieved variables captured patterns of spatial variability comparable to, and locally more accurate than, spatially interpolated meteorological observations. Application of multi-temporal maps of these variables are discussed in relation to current epidemiological research on the distribution and abundance of some common disease vectors.

Climate Teleconnections and Recent Patterns of Human and Animal Disease Outbreaks

Background Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Although Rift Valley fever outbreaks have been known to follow periods of above-normal rainfall, the timing of the outbreak events has largely been unknown. Similarly, there is inadequate knowledge on climate drivers of chikungunya outbreaks. We analyze a variety of climate and satellite-derived vegetation measurements to explain the coupling between patterns of climate variability and disease outbreaks of Rift Valley fever and chikungunya. Methods and Findings We derived a teleconnections map by correlating long-term monthly global precipitation data with the NINO3.4 sea surface temperature (SST) anomaly index. This map identifies regional hot-spots where rainfall variability may have an influence on the ecology of vector borne disease. Among the regions are Eastern and Southern Africa where outbreaks of chikungunya and Rift Valley fever occurred 2004–2009. Chikungunya and Rift Valley fever case locations were mapped to corresponding climate data anomalies to understand associations between specific anomaly patterns in ecological and climate variables and disease outbreak patterns through space and time. From these maps we explored associations among Rift Valley fever disease occurrence locations and cumulative rainfall and vegetation index anomalies. We illustrated the time lag between the driving climate conditions and the timing of the first case of Rift Valley fever. Results showed that reported outbreaks of Rift Valley fever occurred after ∼3–4 months of sustained above-normal rainfall and associated green-up in vegetation, conditions ideal for Rift Valley fever mosquito vectors. For chikungunya we explored associations among surface air temperature, precipitation anomalies, and chikungunya outbreak locations. We found that chikungunya outbreaks occurred under conditions of anomalously high temperatures and drought over Eastern Africa. However, in Southeast Asia, chikungunya outbreaks were negatively correlated (p<0.05) with drought conditions, but positively correlated with warmer-than-normal temperatures and rainfall. Conclusions/Significance Extremes in climate conditions forced by the El Niño/Southern Oscillation (ENSO) lead to severe droughts or floods, ideal ecological conditions for disease vectors to emerge, and may result in epizootics and epidemics of Rift Valley fever and chikungunya. However, the immune status of livestock (Rift Valley fever) and human (chikungunya) populations is a factor that is largely unknown but very likely plays a role in the spatial-temporal patterns of these disease outbreaks. As the frequency and severity of extremes in climate increase, the potential for globalization of vectors and disease is likely to accelerate. Understanding the underlying patterns of global and regional climate variability and their impacts on ecological drivers of vector-borne diseases is critical in long-range planning of appropriate disease and disease-vector response, control, and mitigation strategies.