Drought in Africa: diagnosing demand side drivers

Drought can be defined as a sustained and impactful departure from the normal balance of supply of moisture from the atmosphere to the surface and the demand for surface moisture back to the atmosphere, in favor of the demand side. At regional scales, the supply flux is precipitation; the demand is driven by the “thirst of the atmosphere,” or atmospheric evaporative demand (E₀). Clearly, diagnosing the land-atmosphere dynamics underpinning anomalies in E₀ is essential to a holistic understanding of what drives drought. While E₀ anomalies are driven by anomalies in moisture availability, E₀ reacts quickly and so is a robust drought indicator. Thus, asking to what extent each meteorological driver determines E₀ in drought conditions is of value both academically and operationally.

Here we seek to answer this question in a region that has as yet been ill-served by such analyses due to poor parameterizations of E₀ and the limited availability of data. Previously, drought and famine early warning monitors have here either relied on physically poor process representations of E₀ or on climatological mean estimates. However, by exploiting the advent of long-term, spatially distributed, and accurate reanalyses of the land-atmosphere system and its drivers we can put new data to use to save livelihoods and lives by improving drought monitoring in this data-sparse region.

In this study, we use a new E₀ dataset to decompose anomalies of E₀ during periods of drought into contributions from all of its input variables—temperature, humidity shortwave radiation, and windspeed. We present the (i) general methodology for both the development of E₀ and its decomposition and (ii) the results of the decomposition of E₀ anomalies during drought periods across the continent of Africa. For our fully physical metric of E₀, we developed a nearly 42-year long, daily, global dataset of Penman-Monteith reference evapotranspiration. This new E₀ dataset is driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)—an accurate, fine-resolution land-surface/atmosphere reanalysis. We verified the accuracy of the dataset against (i) point-estimates of E₀ derived by Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) initiative in Southern Africa, a region with sparse ground-truth data and significant humanitarian need, and (ii) on a spatially distributed basis against E₀ derived from other reanalyses (Global Data Assimilation System and Princeton Global Forcing) that, although global, are otherwise unsuitable for operational food-security decision-making.

We determine drought periods using spatially distributed drought-monitoring tools, such as the Normalized Difference Vegetation Index (NDVI), the Evaporative Demand Drought Index (EDDI), and the Standardized Precipitation Index (SPI). Within these drought periods, we conduct a first-order analysis of the anomalies in E₀. This technique assumes that the contributions from anomalies in all drivers sum to the anomaly in E₀; each driver’s contribution is the product of the sensitivity of E₀ to, and the anomaly in, the driver. As our expression for E₀ (i.e., Penman-Monteith reference evapotranspiration) is differentiable, the sensitivity to each driver can be derived explicitly by partial differentiation. Drivers’ anomalies are observed by querying the MERRA-2 reanalysis during drought periods and deriving deviations from the drivers’ long-term means for the same periods across the entire reanalysis period.