A pan-African high-resolution drought index dataset

Jian Peng,Chris Funk,Diego G Miralles,Feyera Hirpa,Sergio M Vicente-Serrano,Simon Dadson,Ellen Dyer,Thomas Lees

doi:10.5194/essd-12-753-2020

Abstract

Abstract. Droughts in Africa cause severe problems, such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security on Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectional perspective that includes crops, hydrological systems, rangeland and environmental systems. Such assessments are essential for policymakers, their advisors and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high-resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole of Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time Series (TS) datasets, Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project and root zone soil moisture modelled by GLEAM. Agreement found between coarse-resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with an average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information for the study of drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) via the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a).

Highlights

Drought is a complex phenomenon that affects natural environments and socioeconomic systems in the world (von Hardenberg et al, 2001; Vicente-Serrano, 2007; Van Loon, 2015; Wilhite and Pulwarty, 2017)
It should be noted that the Standardized Precipitation Evapotranspiration Index (SPEI) over sparsely vegetated and barren areas were masked out based on the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) (Friedl et al, 2010) because SPEI is not reliable over these areas (Beguería et al, 2010, 2014; Zhao et al, 2017)
As expected, the spatial resolution based on CHIRPS and GLEAM (SPEI-HR) shows much more spatial detail that, as a result, reflects mesoscale geographic and climatic features, which highlights the advantages of this new dataset

Summary

Introduction

Drought is a complex phenomenon that affects natural environments and socioeconomic systems in the world (von Hardenberg et al, 2001; Vicente-Serrano, 2007; Van Loon, 2015; Wilhite and Pulwarty, 2017). Droughts can typically be grouped into five types: meteorological (precipitation deficiency), agricultural (soil moisture deficiency), hydrological (runoff and/or groundwater deficiency), socioeconomic (social response to water supply and demand) and environmental or ecologic (Keyantash and Dracup, 2002; AghaKouchak et al, 2015; Crausbay et al, 2017) These different drought categories involve different event characteristics in terms of timing, intensity, duration and spatial extent, making it very difficult to characterize droughts quantitatively (Panu and Sharma, 2002; LloydHughes, 2014; Vicente-Serrano, 2016). Compared to SPI, it considers the precipitation supply and the atmospheric evaporative demand (Beguería et al, 2010; Vicente-Serrano et al, 2012b) This makes the index more informative of the actual drought effects over various natural systems and socioeconomic sectors This makes the index more informative of the actual drought effects over various natural systems and socioeconomic sectors (VicenteSerrano et al, 2012b; Bachmair et al, 2016, 2018; Kumar et al, 2016; S. Sun et al, 2016, 2018; Peña-Gallardo et al, 2018a, b)

Methods

Results

Conclusion