Examining rural Sahelian out-migration in the context of climate change: An analysis of the linkages between rainfall and out-migration in two Malian villages from 1981 to 2009

Subsistence farmers in Sahelian Africa are highly vulnerable to the rainfall effects associated with climate change. Permanent or temporary out-migration can provide an individual or family the opportunity to mitigate against these effects. One major challenge to quantifying the impact of climate change on out-migration is lack of appropriate spatial and temporal data. Out-migration data must be adequately detailed to include both long- and short-term departures. The climate data must provide fine scale, community-specific detail. To examine the climate variability as a factor of out-migration we examine individual- and community-level responses using highly detailed, full migration histories of 3150 individuals with fine-scale rainfall data. Using this multi-method approach we examine the probability of out-migration as well as out-migration duration and destination as they relate to locally measured rainfall. The results suggest that out-migration behavior does not generally change because of failures or variation in the rainy season

Exploring trends in wet-season precipitation and drought indices in wet, humid and dry regions

This study examines wet season droughts using eight products from the Frequent Rainfall Observations on GridS database. The study begins by evaluating wet season precipitation totals and wet day counts at seasonal and decadal time scales. While we find a high level of agreement among the products at a seasonal time scale, evaluations of 10 year variability indicate substantial non-stationary inter-product differences that make the assessment of low-frequency changes difficult, especially in data-sparse regions. Some products, however, appear more reliable than others on decadal time scales. Global time series of dry, middle, and wet region standardized precipitation index time series indicate little coherent change. There is substantial coherence in year-to-year variations in these time series for the better-performing products, likely indicative of skill for monitoring variations at large spatial scales. During the wet season, the data do not appear to indicate widespread global changes in precipitation, reference evapotranspiration (RefET) or Standardized Precipitation Evapotranspiration Index (SPEI) values. These data also do not indicate a global shift towards increasing aridity. Focusing on SPEI values for dry regions during droughts, however, we find modest increases in RefET and decreases in SPEI when wet season precipitation is below normal. Dry region SPEI values during droughts have decreased by −0.2 since the 1990s. The cause of these RefET increases is unclear, and more detailed analysis will be needed to confirm these results. For wet regions, however, the majority of products appear to indicate increases in wet season precipitation, although many products perform poorly in these regions due to limited observation networks, and estimated increases vary substantially. Synopsis: Our analysis indicates a lack of increasing aridity at global scales, issues associated with non-stationary systematic errors, and concerns associated with increases in reference evapotranspiration in global dry regions during droughts.US Geological Survey's Drivers of Drought program; USAID Famine Early Warning Systems Network United States Agency for International Development (USAID); USAID/NASA Harvest program; Australian Research Council [CE170100023, DP160103439]Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

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

Sending out an SOS: using start of rainy season indicators for market price forecasting to support famine early warning

We examine relationships between the start of rainy season (SOS) and sub-national grain (white maize) market price movements in five African countries. Our work is motivated by three factors: (a) some regions are seeing increasing volatility SOS timing; (b) SOS represents the first observable occurrence in the agricultural season and starts a chain reaction of decisions that influence planting, labor allocation, and harvest—all of which can have direct impacts on local food prices and availability; and (c) pre- and post-harvest price movements provide key insights into supply-and-demand issues related to food insecurity. We start by exploring a number of different SOS definitions using varying reference periods to define whether an SOS is ‘on-time’ or ‘late’. We then compare how those different definitions perform in seasonal price forecasting models. Specifically, we examine if SOS indicators can predict price means over 6 and 9 month periods, or roughly the length of time from planting to market. We use different reference periods for defining ‘early’ versus ‘late’ seasonal starts based on the previous year’s start date, or median start dates over the past 3, 5, and 10 year periods. We then compare the out-of-sample forecast performance of univariate time-series models (autoregressive integrated moving average (ARIMA)) with time-series (ARIMAX) models that include various SOS definitions as exogenous predictors. We find that using some form of SOS indicator (either an SOS anomaly or 1st month’s rainfall anomaly) leads to increased predictive power when examining prices over a 6 months window. However, the results vary considerably by country. We find the strongest performance of SOS indicators in central Ethiopia, southern Kenya, and southern Somalia. We find less evidence in support of the use of SOS indicators for price forecasting in Malawi and Mozambique

Farmer Perception, Recollection, and Remote Sensing in Weather Index Insurance: An Ethiopia Case Study

A challenge in addressing climate risk in developing countries is that many regions have extremely limited formal data sets, so for these regions, people must rely on technologies like remote sensing for solutions. However, this means the necessary formal weather data to design and validate remote sensing solutions do not exist. Therefore, many projects use farmers’ reported perceptions and recollections of climate risk events, such as drought. However, if these are used to design risk management interventions such as insurance, there may be biases and limitations which could potentially lead to a problematic product. To better understand the value and validity of farmer perceptions, this paper explores two related questions: (1) Is there evidence that farmers reporting data have any information about actual drought events, and (2) is there evidence that it is valuable to address recollection and perception issues when using farmer-reported data? We investigated these questions by analyzing index insurance, in which remote sensing products trigger payments to farmers during loss years. Our case study is perhaps the largest participatory farmer remote sensing insurance project in Ethiopia. We tested the cross-consistency of farmer-reported seasonal vulnerabilities against the years reported as droughts by independent satellite data sources. We found evidence that farmer-reported events are independently reflected in multiple remote sensing datasets, suggesting that there is legitimate information in farmer reporting. Repeated community-based meetings over time and aggregating independent village reports over space lead to improved predictions, suggesting that it may be important to utilize methods to address potential biases

The effects of El Niño on agricultural water balance in Guatemala

Poster -- Universidad de Costa Rica. Centro de Investigaciones Geofísicas, 2011. Presented at the 2010 Fall Meeting, American Geophysical Union, San Francisco, California, USA. 13-17th, December.More than half the population of Guatemala lives in rural areas and depends on subsistence agriculture for their well being. This region is vulnerable to many climatic events, one of which is El Niño. This study looks at the effects of El Nino on rainfall patterns at regional scales and specifically quantifies the effects on agricultural water balances in Guatemala. Analysis is focused on maize crops during the Primera growing season (May – Aug, May -Oct). The study builds on rainfall and water balance modeling techniques developed by the Famine Early Warning Systems Network (FEWS NET). The results corroborate previous work, showing that there is a negative relationship between El Niño and rainfall, primarily on the Pacific side of the region and mainly during the months of August and September. The study also found that the related rainfall variations influence long-term (May - October) maize growing areas and could affect the start of the short-term Postrera season (August - October) by extending the Canícula (mid season dry period).U.S. Geological Survey (USGS)- Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD. FEWS NET , Climate Hazard Group- UCSB/Geography dpt., UCSB/Geography dpt , Universidad de Costa Rica, Famine Early Warning System Network (FEWS NET)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

A global long-term daily reanalysis of reference evapotranspiration for drought and food-security monitoring

Abstract NOAA has developed a global reference evapotranspiration (ET0) reanalysis using the UN Food and Agriculture Organization formulation (FAO-56) of the Penman-Monteith equation forced by MERRA phase 2 (MERRA2) meteorological and radiative drivers. The NOAA ET0 reanalysis is provided daily from January 1, 1980 to the near-present at a resolution of 0.5° latitude × 0.625° longitude. The reanalysis is verified against station data across southern Africa, a region presenting both significant challenges regarding hydroclimatic variability and observational quantity and quality and significant potential benefits to food-insecure populations. These data are generated from observations from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) network. We further verified globally against spatially distributed ET0 derived from two reanalyses–the Global Data Assimilation System (GDAS) and Princeton Global Forcing (PGF)–and these verifications produced similar results, yet demonstrated wide regional and seasonal differences. We also present cases that verify the operational applicability of the reanalysis in long-established drought, famine, crop- and pastoral-stress metrics, and in predictability assessments of drought forecasts

El Niño-Southern Oscillation diversity and Southern Africa teleconnections during Austral Summer

A wide range of sea surface temperature (SST) expressions have been observed during the El Niño–Southern Oscillation events of 1950–2010, which have occurred simultaneously with different global atmospheric circulations. This study examines the atmospheric circulation and precipitation during December–March 1950–2010 over the African Continent south of 15 S, a region hereafter known as Southern Africa, associated with eight tropical Pacific SST expressions characteristic of El Niño and La Niña events. The self-organizing map method along with a statistical distinguishability test was used to isolate the SST expressions of El Niño and La Niña. The seasonal precipitation forcing over Southern Africa associated with the eight SST expressions was investigated in terms of the horizontal winds, moisture budget and vertical motion. El Niño events, with warm SST across the east and central Pacific Ocean and warmer than average SST over the Indian Ocean, are associated with precipitation reductions over Southern Africa. The regional precipitation reductions are forced primarily by large-scale mid-tropospheric subsidence associated with anticyclonic circulation in the upper troposphere. El Niño events with cooler than average SST over the Indian Ocean are associated with precipitation increases over Southern Africa associated with lower tropospheric cyclonic circulation and mid-tropospheric ascent. La Niña events, with cool SST anomalies over the central Pacific and warm SST over the west Pacific and Indian Ocean, are associated with precipitation increases over Southern Africa. The regional precipitation increases are forced primarily by lower tropospheric cyclonic circulation, resulting in mid-tropospheric ascent and an increased flux of moisture into the region