A pan-African high-resolution drought index dataset
<p>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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-03-01
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| Series: | Earth System Science Data |
| Online Access: | https://www.earth-syst-sci-data.net/12/753/2020/essd-12-753-2020.pdf |
| Summary: | <p>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 (<span class="inline-formula"><i>R</i></span>) 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: <a href="https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb">https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb</a>
(Peng et al., 2019a).</p> |
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| ISSN: | 1866-3508 1866-3516 |