Search

The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. Interventions to mitigate food insecurity impacts require early warning of droughts – preferably as early as possible before the harvest season (typically starting in April) and lean season (typically starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available root-zone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of RZSM products from the recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). Results show that the NHyFAS products would have identified the regional severe drought event – which peaked during December–February of 2015–2016 – at least as early as 1 November 2015. Next, it is shown that during 1982–2016, February RZSM (Feb-RZSM) forecasts (monitoring product) available in early November (early March) have a correlation of 0.49 (0.79) with the detrended regional crop yield. It is also found that when the February RZSM forecast (monitoring product) available in early November (early March) is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds of the time (always), at least over the sample years considered. Additionally, it is shown that the February RZSM forecast (monitoring product) can provide "out-of-sample" crop yield forecasts with comparable (substantially better with 40 % reduction in mean error) skill to December–February ENSO. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region. Finally, since a framework similar to NHyFAS can be used to provide RZSM monitoring and forecasting products over other regions of the globe, this case study also demonstrates potential for supporting food insecurity early warning globally. [ABSTRACT FROM AUTHOR]

West Africa is one of the poorest regions in the world and highly vulnerable to extreme hydrological events due to the lack of reliable monitoring and forecast systems. For the first time, we demonstrate that initial hydrological conditions informed by satellite‐based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts. TWS variability detected by the Gravity Recovery and Climate Experiment (GRACE) satellites is assimilated into a land surface model during 2003–2016 and used to initialize 6‐month hindcasts (i.e., forecasts of past events) during West Africa's wet seasons. We find that GRACE data assimilation (DA) generally increases groundwater and soil moisture storage in the region, resulting in increased evapotranspiration and reduced total runoff. Total runoff is particularly lower at the headwaters of the Niger River, positively impacting streamflow simulations and hindcast initializations. Compared to simulations without GRACE‐DA, hindcasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin, which is consistent with GRACE's coarse spatial resolution. The long memory of groundwater and deep soil moisture, two main TWS components updated by GRACE‐DA, is reflected in prolonged improvements in the streamflow hindcasts. Model accuracy at Niamey, Niger, the most populated city where streamflow observations are available, improved up to 33% during the flood season. These new findings directly contribute to ongoing developments in food security, flood potential forecast, and water‐related disaster warning systems for Africa. [ABSTRACT FROM AUTHOR]

The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. In 2015–2016, an El Niño-driven drought resulted in major maize production shortfalls, food price increases, and livelihood disruptions that pushed 29 million people into severe food insecurity. Interventions to mitigate food insecurity impacts require early warning of droughts – preferably as early as possible before the harvest season (typically, starting in April) and lean season (typically, starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available rootzone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of a recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). The NHyFAS system's ability to forecast and monitor the 2015/2016 drought event is evaluated. The system's capacity to explain interannual variations in regional crop yield and identify below-normal crop yield events is also evaluated. Results show that the NHyFAS products would have identified the regional severe drought event, which peaked during December–February of 2015/2016, at least as early as 1 November 2015. Next, it is shown that February RZSM forecasts produced as early as 1 November (4–5 months before the start of harvest and about a year before the start of the next lean season) correlate fairly well with regional crop yields (r = 0.49). The February RZSM monitoring product, available in early March, correlates with the regional crop yield with higher skill (r = 0.79). It is also found that when the February RZSM forecast produced on November 1 is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds (significance level ~ 86 %) of the time. Furthermore, when the February RZSM monitoring product (available in early March) indicates a lowest tercile value, the crop yield is always below normal, at least over the sample years considered. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region. [ABSTRACT FROM AUTHOR]

The article focuses on the history of anthropogenic warming and drought in Eutophia and Southern Africa in 2015 brought by El Niño and local air temperatures. Topics discussed include recurrent soil moisture and runoff deficits experienced by Ethiopian highlands since 1990, warm phase of El Niño-Southern Oscillation (ENSO) from 1997-2013, and rainfall variance observed from 2015-2016.

This paper examined the relationship between birth weight, precipitation, and temperature in 19 African countries. We matched recorded birth weights from Demographic and Health Surveys covering 1986 through 2010 with gridded monthly precipitation and temperature data derived from satellite and ground-based weather stations. Observed weather patterns during various stages of pregnancy were also used to examine the effect of temperature and precipitation on birth weight outcomes. In our empirical model we allowed the effect of weather factors to vary by the dominant food production strategy (livelihood zone) in a given region as well as by household wealth, mother's education and birth season. This allowed us to determine if certain populations are more or less vulnerable to unexpected weather changes after adjusting for known covariates. Finally we measured effect size by observing differences in birth weight outcomes in women who have one low birth weight experience and at least one healthy birth weight baby. The results indicated that climate does indeed impact birth weight and at a level comparable, in some cases, to the impact of increasing women's education or household electricity status. [ABSTRACT FROM AUTHOR]

Acute and chronic water scarcity impacts four billion people, a number likely to climb with population growth and increasing demand for food and energy production. Chronic water insecurity and long-term trends are well studied at the global and regional level; however, there have not been adequate systems in place for routinely monitoring acute water scarcity. To address this gap, we developed a monthly monitoring system that computes annual water availability per capita based on hydrologic data from the Famine Early Warning System Network (FEWS NET) Land Data Assimilation System (FLDAS) and gridded population data from WorldPop. The monitoring system yields maps of acute water scarcity using monthly Falkenmark classifications and departures from the long-term mean classification. These maps are designed to serve FEWS NET monitoring objectives; however, the underlying data are publicly available and can support research on the roles of population and hydrologic change on water scarcity at sub-annual and sub-national scales. [ABSTRACT FROM AUTHOR]

A seamless and effective early warning system is critical for mitigating the most adverse impacts of water and food insecurity in Africa. The U.S. Agency for International Development (USAID) supported Famine Early Warning Systems Network (FEWS NET) is tasked with providing early warning and analysis on acute food and water insecurity to help government decision-makers and relief agencies plan for and respond to resulting humanitarian crises. This presentation describes a recently developed multimodel based seasonal scale hydrologic forecasting system, which has been operationally supporting the early warning efforts of FEWS NET since August 2018. Since then, this system has helped identify flood risk potential in parts of Eastern and Western Africa, and development and progression of drought conditions in Southern Africa. Both of these types of extremes contribute to food and water insecurity. At the core of this system is NASA's Land Information System (LIS) (a hydrologic modeling framework), which supports seasonal scale monitoring and forecasts of runoff and soil moisture by combining satellite and reanalysis based atmospheric forcings datasets and dynamical climate forecasts. Specifically, the LIS-based seasonal scale hydrologic forecasting system incorporates NASA/GMAO's Catchment and the Noah Multi-Physics (Noah-MP) land surface models (LSMs). It is driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and the UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall dataset to provide a long historic record for monitoring (1982-present) of hydrologic variables. Currently, the seasonal scale hydrological forecasts are generated by using NASA's Goddard Earth Observing System Model, version 5 (GEOS-5).Long-term availability of monitoring and forecasts of hydrologic variables allows for historical validation of this dataset against independent hydrologic observations and reported crop yields (up to the extent they are available). Validation results thus far have demonstrated agreement of hydrologic monitoring simulations with independent satellite based observations in Eastern Africa and the value of this system in supporting food insecurity early warning in Southern Africa.This presentation will first describe the development of this system, followed by its validation for drought early warning and subsequently the process of stakeholder engagement and operational application of the products derived from this system by the FEWS NET. [ABSTRACT FROM AUTHOR]

• A hydrologic forecasting framework is built to predict drought and flood potentials for food insecurity early warning. • Hydrologic forecasts are improved by using entire suite of NMME total-precipitation forecasts in land surface modeling. • The higher skill of the entire suite of NMME precipitation forecast is also reflected in predicting hydrologic drought. In situ hydrologic monitoring over regions most susceptible to food insecurity can be a challenge in current times due to various socio-economic and political issues in combination with environmental factors such as ongoing famine or drought. Hydrologic monitoring and initializing forecasts based on remotely sensed and analyzed data can contribute significantly to early warning in such regions. Routine hydrologic forecasts, as provided by NASA's Hydrologic Forecasting and Analysis System (NHyFAS), are a recent addition to early warning systems. A custom instance of NHyFAS, termed FLDAS-Forecast, is used by FEWS NET's Land Data Assimilation System (FLDAS). The FLDAS-Forecast's dynamic forecasting component was originally set up with Goddard Earth Observing System (GEOS) forecast inputs and has been recently expanded with precipitation forecast forcing from the North American Multi-Model Ensemble (NMME). This paper describes the improvements in seasonal hydrologic forecasts produced with this updated system. Evaluations in this study focus on soil moisture across southern Africa's growing season. Soil moisture forecasts are benchmarked and evaluated relative to climatology-based forecasts and historic runs, which are driven by observation-based meteorological forcing fields, and they are verified with remotely sensed observations of soil moisture and vegetation. Through multiple deterministic and probabilistic skill assessments, we show that using the larger ensemble of NMME precipitation inputs in the forecast system results in higher quality hydrologic forecasts than are allowed by climatology- or GEOS-only-based forecasts. Further, the near-real-time NMME-based rootzone soil moisture forecasts were able to correctly predict developing drought conditions over southern Africa through late 2019 and into early 2020. [ABSTRACT FROM AUTHOR]