Monitoring Drought Across Many Scales

Monitoring drought across many scales Chris Funk As gas and food prices increase while per capita harvested area decreases, drought and disruptions in food availability exert more and more pressure on the political and economic stability of ‘frailed’ states. Improved drought monitoring across many spatial and temporal time scales has therefore become increasingly important. As this need mounts, so have our capacities to observe and understand the earth’s climate. Relatively new satellite systems, such as the Moderate Resolution Imaging Spectrometer, allow us to watch the earth at scales of ~100 meters. Improved rainfall retrievals give us more timely and accurate observations of hydrologic extremes. Web-based mapping and analysis tools help us integrate and utilize this information in ‘actionable’ ways. Over the past few years, scientists at the US Geological Survey and the University of California, Santa Barbara’s Climate Hazard Group have developed new monitoring datasets, tools and methods supporting the monitoring of drought across South America, Africa and Asia. This talk summarizes these new products, and sets out some general principles that will help us to identify agricultural droughts in rainfed environments. Special attention is given to monitoring and understanding low frequency changes in climate over and around the Indian Ocean during boreal spring and summer. This work links ‘bottom up’ evaluations of terrestrial drying trends with ‘top down’ diagnostic analyses tracing the associated changes in atmospheric thermodynamics and moisture transports. The resulting framework for ‘drought forensics’ is helping us to understand and prepare for near-term climate changes. As the south-central Indian Ocean (SIO) has warmed beneath rapid surface winds, SIO evaporation and rainfall have increased dramatically, setting up overturning circulations helping to lower rainfall across east Africa and India. Current collaboration with USAID links this research with climate adaptation and the identification of emergent at-risk populations

Declining global per capita agricultural production and warming oceans threaten food security

Despite accelerating globalization, most people still eat food that is grown locally. Developing countries with weak purchasing power tend to import as little food as possible from global markets, suffering consumption deficits during times of high prices or production declines. Local agricultural production, therefore, is critical to both food security and economic development among the rural poor. The level of local agricultural production, in turn, will be determined by the amount and quality of arable land, the amount and quality of agricultural inputs (fertilizer, seeds, pesticides, etc.), as well as farm-related technology, practices and policies. This paper discusses several emerging threats to global and regional food security, including declining yield gains that are failing to keep up with population increases, and warming in the tropical Indian Ocean and its impact on rainfall. If yields continue to grow more slowly than per capita harvested area, parts of Africa, Asia and Central and Southern America will experience substantial declines in per capita cereal production. Global per capita cereal production will potentially decline by 14% between 2008 and 2030. Climate change is likely to further affect food production, particularly in regions that have very low yields due to lack of technology. Drought, caused by anthropogenic warming in the Indian and Pacific Oceans, may also reduce 21st century food availability in some countries by disrupting moisture transports and bringing down dry air over crop growing areas. The impacts of these circulation changes over Asia remain uncertain. For Africa, however, Indian Ocean warming appears to have already reduced rainfall during the main growing season along the eastern edge of tropical Africa, from southern Somalia to northern parts of the Republic of South Africa. Through a combination of quantitative modeling of food balances and an examination of climate change, this study presents an analysis of emerging threats to global food security

Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Nio, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations

A 10-year literature review of family caregiving for motor neurone disease: Moving from caregiver burden studies to palliative care interventions

Background: There is growing awareness that different terminal diseases translate into different family caregiver experiences, and the palliative and supportive care needs of these families are both similar and unique. Family members caring for people with motor neurone disease may experience exceptional strain due to the usually rapid and progressive nature of this terminal illness. Aim: The purpose of this review is to synthesize contemporary research and provide a comprehensive summary of findings relevant to motor neurone disease family caregivers, as well as highlight some of the suggested interventions to alleviate burden and improve quality of life for this group. Design: We conducted a comprehensive review of empirical research on family caregiving for people with motor neurone disease in peer-reviewed journals published in English, January 2000–April 2011. Fifty-nine studies met the inclusion criteria. Results: This comprehensive literature review was consistent with previous research documenting the substantial burden and distress experienced by motor neurone disease family caregivers and revealed important points in the trajectory of care that have the potential for negative effects. The diagnosis experience, assisted ventilation, cognitive changes and end-of-life decision making create challenges within a short time. This review has also implicated the need for improvements in access to palliative care services and highlighted the absence of interventions to improve care. Conclusions: Caregiver burden and quality-of-life studies on motor neurone disease family caregivers have so far dominated the research landscape .The focus needs to be on developing interventions that provide direct practical and psychosocial supports for motor neurone disease family caregivers

Spatial and temporal variation in population dynamics of Andean frogs: Effects of forest disturbance and evidence for declines

AbstractBiodiversity loss is a global phenomenon that can result in the collapse of food webs and critical ecosystem services. Amphibian population decline over the last century is a notable case of species loss because amphibians survived the last four major extinction events in global history, their current rate of extinction is unprecedented, and their rate of extinction is greater than that for most other taxonomic groups. Despite the severity of this conservation problem and its relevance to the study of global biodiversity loss, major knowledge gaps remain for many of the most threatened species and regions in the world. Rigorous estimates of population parameters are lacking for many amphibian species in the Neotropics. The goal of our study was to determine how the demography of seven species of the genus Pristimantis varied over time and space in two cloud forests in the Ecuadorian Andes. We completed a long term capture–mark–recapture study to estimate abundance, survival, and population growth rates in two cloud forests in the Ecuadorian Andes; from 2002 to 2009 at Yanayacu in the Eastern Cordillera and from 2002 to 2003 at Cashca Totoras in the Western Cordillera. Our results showed seasonal and annual variation in population parameters by species and sex. P. bicantus experienced significant reductions in abundance over the course of our study. Abundance, apparent survival, and population growth rates were lower in disturbed than in primary or mature secondary forest. The results of our study raise concerns for the population status of understudied amphibian groups and provide insights into the population dynamics of Neotropical amphibians

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast perio

Modeling rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall-Southern Malawi case study

During 1990s, disaster risk reduction emerged as a novel, proactive approach to managing risks from natural hazards. The World Bank, USAlD, and other international donor agencies began making efforts to mainstream disaster risk reduction in countries whose population and economies were heavily dependent on rain-fed agriculture. This approach has more significance in light of the increasing climatic hazard patterns and the climate scenarios projected for different hazard prone countries in the world. The Famine Early Warning System Network (FEWS NET) has been monitoring the food security issues in the sub-Saharan Africa, Asia and in Haiti. FEWS NET monitors the rainfall and moisture availability conditions with the help of NOAA RFE2 data for deriving food security status in Africa. This paper highlights the efforts in using satellite estimated rainfall inputs to develop drought vulnerability models in the drought prone areas in Malawi. The satellite RFE2 based SPI corresponding to the critical tasseling and silking phases (in the months of January, February, and March) were statistically regressed with drought-induced yield losses at the district level. The analysis has shown that the drought conditions in February and early March lead to most damage to maize yields in this region. The district-wise vulnerabilities to drought were up scaled to obtain a regional maize vulnerability model for southern Malawi. The results would help in establishing an early monitoring mechanism for drought impact assessment, give the decision makers additional time to assess seasonal outcomes, and identify potential food-related hazards in Malawi

Nine challenges in incorporating the dynamics of behaviour in infectious diseases models.

Traditionally, the spread of infectious diseases in human populations has been modelled with static parameters. These parameters, however, can change when individuals change their behaviour. If these changes are themselves influenced by the disease dynamics, there is scope for mechanistic models of behaviour to improve our understanding of this interaction. Here, we present challenges in modelling changes in behaviour relating to disease dynamics, specifically: how to incorporate behavioural changes in models of infectious disease dynamics, how to inform measurement of relevant behaviour to parameterise such models, and how to determine the impact of behavioural changes on observed disease dynamics