Advancing early warning capabilities with CHIRPS-compatible NCEP GEFS precipitation forecasts.

CHIRPS-GEFS is an operational data set that provides daily bias-corrected forecasts for next 1-day to ~15-day precipitation totals and anomalies at a quasi-global 50-deg N to 50-deg S extent and 0.05-degree resolution. These are based on National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) precipitation forecasts. CHIRPS-GEFS forecasts are compatible with Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) data, which is actively used for drought monitoring, early warning, and near real-time impact assessments. A rank-based quantile matching procedure is used to transform GEFS v12 "reforecast" and "real-time" forecast ensemble means to CHIRPS spatial-temporal characteristics. Matching distributions to CHIRPS makes forecasts better reflect local climatology at finer spatial resolution and reduces moderate-to-large forecast errors. As shown in this study, having a CHIRPS-compatible version of the latest generation of NCEP GEFS forecasts enables rapid assessment of current forecasts and local historical context. CHIRPS-GEFS effectively bridges the gap between observations and weather predictions, increasing the value of both by connecting monitoring resources (CHIRPS) with interoperable forecasts.

Exploring Strategies for Investigating the Mechanisms Linking Climate and Individual-Level Child Health Outcomes: An Analysis of Birth Weight in Mali.

The goal of this article is to consider data solutions to investigate the differential pathways that connect climate/weather variability to child health outcomes. We apply several measures capturing different aspects of climate/weather variability to different time periods of in utero exposure. The measures are designed to capture the complexities of climate-related risks and isolate their impacts based on the timing and duration of exposure. Specifically, we focus on infant birth weight in Mali and consider local weather and environmental conditions associated with the three most frequently posited potential drivers of adverse health outcomes: disease (malaria), heat stress, and food insecurity. We focus this study on Mali, where seasonal trends facilitate the use of measures specifically designed to capture distinct aspects of climate/weather conditions relevant to the potential drivers. Results indicate that attention to the timing of exposures and employing measures designed to capture nuances in each of the drivers provides important insight into climate and birth weight outcomes, especially in the case of factors impacted by precipitation. Results also indicate that high temperatures and low levels of agricultural production are consistently associated with lower birth weights, and exposure to malarious conditions may increase likelihood of nonlive birth outcomes.

A slow rainy season onset is a reliable harbinger of drought in most food insecure regions in Sub-Saharan Africa.

Since 2015, Sub-Saharan Africa (SSA) has experienced an unprecedented rise in acute food insecurity (AFI), and current projections for the year 2020 indicate that more than 100 million Africans are estimated to receive emergency food assistance. Climate-driven drought is one of the main contributing factors to AFI, and timely and appropriate actions can be taken to mitigate impacts of AFI on lives and livelihoods through early warning systems. To support this goal, we use observations of peak Normalized Difference Vegetation Index (NDVI) as an indicator of seasonal drought conditions following a rainy season to show that delays in the onset of the rainy season (onset date) can be an effective early indicator of seasonal drought conditions. The core of this study is an evaluation of the relationship of the onset dates and peak NDVI, stratified by AFI risks, calculated using AFI reports by the United States Agency of International Development (USAID)-funded Famine Early Warning Systems Network (FEWS NET). Several parts of SSA, mostly located in East Africa (EA), reported the "Crisis" phase of AFI-requiring emergency food assistance-at least one-third of the time between April 2011 to present. The results show that the onset date can effectively explain much of the interannual variability in peak NDVI in the regions with the highest AFI risk level, particularly in EA where the median of correlation (across all the Administrative Unit 2) varies between -0.42 to -0.68. In general, an onset date delay of at least 1 dekad (10 days) increases the likelihood of seasonal drought conditions. In the regions with highest risks of AFI, an onset delay of just 1 dekad doubles the chance of the standardized anomaly of peak NDVI being below -1, making a -1 anomaly the most probable outcome. In those regions, a 2-dekads delay in the onset date is associated with a very high probability (50%) of seasonal drought conditions (-1 standardized anomaly of NDVI). Finally, a multivariate regression analysis between standardized anomaly and onset date anomaly further substantiates the negative impacts of delay in onset date on NDVI anomaly. This relationship is statistically significant over the SSA as a whole, particularly in the EA region. These results imply that the onset date can be used as an additional critical tool to provide alerts of seasonal drought development in the most food-insecure regions of SSA. Early warning systems using onset date as a tool can help trigger effective mid-season responses to save human lives, livestock, and livelihoods, and, therefore, mitigate the adverse impacts of drought hazards.

Financing agricultural drought risk through ex-ante cash transfers.

Despite advances in drought early warning systems, forecast information is rarely used for triggering and financing early actions, such as cash transfer. Scaling up cash transfer pay-outs, and overcoming the barriers to actions based on forecasts, requires an understanding of costs resulting from False Alarms, and the potential benefits associated with appropriate early interventions. On this study, we evaluate the potential cost-effectiveness of cash transfer responses, comparing the relative costs of ex-ante cash transfers during the maize growing season to ex-post cash transfers after harvesting in Kenya. For that, we developed a forecast model using Fast-and Frugal Trees that unravels early warning relationships between climate variability, vegetation coverage, and maize yields at multiple lead times. Results indicate that our models correctly forecast low maize yield events 85% of the time across the districts studied, some already six months before harvesting. The models' performance improves towards the end of the growing season driven by a decrease of 29% in the probability of False Alarms. Overall, we show that timely cash transfers ex-ante to a disaster can often be more cost-effective than investing in ex-post expenditures. Our findings suggest that early response can yield significant cost savings, and can potentially increase the effectiveness of existing cash transfer systems.

Combining insights from quantile and ordinal regression: child malnutrition in Guatemala.

Chronic child undernutrition is a persistent problem in developing countries and has been the focus of hundreds of studies where the primary intent is to improve targeting of public health and economic development policies. In national level cross-sectional studies undernutrition is measured as child stunting and the goal is to assess differences in prevalence among population subgroups. Several types of regression modeling frameworks have been used to study childhood stunting but the literature provides little guidance in terms of statistical properties and the ease with which the results can be communicated to the policy community. We compare the results from quantile regression and ordinal regression models. The two frameworks can be linked analytically and together yield complementary insights. We find that reflecting on interpretations from both models leads to a more thorough analysis and forces the analyst to consider the policy utility of the findings. Guatemala is used as the country focus for the study.