ArticlePDF Available

Global drought monitoring with drought severity index (DSI) using Google Earth Engine


Abstract and Figures

Unlike most disasters, drought does not appear abruptly. It slowly builds over time due to the changes in different environmental and climatological factors. It is one of the deadly disasters that has plagued almost every region of the globe since early civilization. Droughts are scientifically being studied with the help of either simple or composite indices. At 500-m spatial resolution, this study presents global scale drought severity index (DSI), a composite index using Moderate Resolution Imaging Spectroradiometer (MODIS), 8-day temporal resolution evapotranspiration (ET), potential evapotranspiration (PET), and normalized difference vegetation index (NDVI). This index is mainly used to identify meteorological droughts and also has proven reliable for studying agriculture droughts. In this study, Google Earth Engine (GEE), a cloud-based geospatial data computational platform, is used for drought mapping and monitoring from 2001 to 2019. For annual DSI spatial maps, the statistical median is computed ranging from − 1 to + 1, which means drought struck or dry regions have values closer to negative, and wet zones have values near to positive. For the validity of DSI results, the findings are compared with available records of droughts struck in previous years. This study declares that continent-wise, Australia, Africa, and Asia have the most extreme and frequent drought events while South America and North America come a close second. Europe is the least affected by this particular weather event when compared to other continents.
Content may be subject to copyright.
1 3
Global drought monitoring withdrought severity index (DSI) using
Google Earth Engine
RamlaKhan1,2 · HammadGilani1
Received: 28 March 2021 / Accepted: 29 June 2021
© The Author(s) 2021
Unlike most disasters, drought does not appear abruptly. It slowly builds over time due to the changes in different environ-
mental and climatological factors. It is one of the deadly disasters that has plagued almost every region of the globe since
early civilization. Droughts are scientifically being studied with the help of either simple or composite indices. At 500-m
spatial resolution, this study presents global scale drought severity index (DSI), a composite index using Moderate Resolution
Imaging Spectroradiometer (MODIS), 8-day temporal resolution evapotranspiration (ET), potential evapotranspiration (PET),
and normalized difference vegetation index (NDVI). This index is mainly used to identify meteorological droughts and also
has proven reliable for studying agriculture droughts. In this study, Google Earth Engine (GEE), a cloud-based geospatial
data computational platform, is used for drought mapping and monitoring from 2001 to 2019. For annual DSI spatial maps,
the statistical median is computed ranging from 1 to + 1, which means droughtstruck or dry regions have values closer to
negative, and wet zones havevalues near to positive. For the validity of DSI results, the findings are compared with avail-
able records of droughts struck in previous years. This study declares that continent-wise, Australia, Africa, and Asia have
the most extreme and frequent drought events while South America and North America come a close second. Europe is the
least affected by this particular weather event when compared to other continents.
1 Introduction
According to a special report, “Managing the Risks of
Extreme Events and Disasters to Advance Climate Change
Adaptation (SREX)”, released by the Intergovernmental
Panel on Climate Change (IPCC) for climate change and
natural disasters, weather extremes and climate events may
not appear harmful individually but their accumulative effect
can be seen in the form of deadly droughts with time (IPCC
2012). During a dry season, the water demand of society and
the available amount falls short (Elhag and Zhang 2018). In
humid areas like the Amazon forest, a mere 10% decrease
in precipitation will not cause any lasting damage, but the
same situation in a semi-arid area like northeast Brazil
will be a definite cause for concern (Food and Agriculture
Organization of the United Nations 2019). Droughts can
affect regions that are not directly hit by it; for example,
if a mountainous area receives less rainfall and snow than
usual and it causes dryness, then the areas which rely on
groundwater and streams that comes from those mountains
will also be subjected to impacts of the dry season (IDMC
2020). Drought is one of the costliest disasters humankind
faces all over the world (Wang etal. 2019). It impacts socio-
economic sectors like tourism, energy, water supply, agri-
culture, energy, infrastructure, and the country’s economy
(Meza etal., 2019). For instance,in the year 2008, concerns
regarding the development of a La Niña event raised alarms
about drought and consequently about a massive shortage
of energy production in the electric power plants in Chile
(Peterson etal. 2009).
In 2018, a global survey was conducted for indicators
and their relevance to drought assessment by collaborating
with the United Nations (UN) university and Global Drought
Observatory (GDO). The research was an expensive and
detailed study, focusing on the agriculture and water sec-
tor, but the summary was that a vast majority of research-
ers voted in favor of indicator-based assessment of drought
(Meza etal.2019). These indicators are numerically com-
puted and introduced by experts working on climate change
* Ramla Khan
1 Department ofSpace Science, Institute ofSpace Technology,
Islamabad44000, Pakistan
2 Environment Earth andEcosystem Sciences (EEES) School,
The Open University, MiltonKeynesMK76AA, UK
/ Published online: 10 August 2021
Theoretical and Applied Climatology (2021) 146:411–427
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
issues. There are three approaches used for drought monitor-
ing through indices: with only one index, a combination of
indices, and hybrid/composite indicators. The single or sim-
ple index requires only one climatic/hydro-meteorological
variable for its computation while a combination of multiple
single indices to formulate a single composite index (WMO
and GWP 2016).
Drought severity index (DSI) is a composite index that
assimilates variables of vegetation and evapotranspiration
(Wang etal. 2019). DSI is categorized mainly for measur-
ing meteorological droughts (Zargar etal. 2011) and can
also be used for observing agriculture drought (Elhag and
Zhang 2018). It was first introduced by Mu etal. (2013)
using the Moderate Resolution Imaging Spectroradiometer
(MODIS) satellite normalized difference vegetation index
(NDVI) and evapotranspiration products for the computa-
tion. However, in this study, instead of taking the mean of
the yearly maps, the statistical median was opted because
it gave clearer and more accurate results. Also, the results
are discussed in much more detail for almost every country
throughout the globe.
Google Earth Engine (GEE) is a cloud-based platform
and runs on Python API and Java scripting (Gorelick etal.
2017). It is a powerful tool for dealing with big geo-datasets.
It is also faster than traditional software and can reduce the
processing time in half. GEE also has many datasets avail-
able in its repository that researchers with limited coding
backgrounds can readily and easily use.
2 Materials andmethods
2.1 Datasets
This study used freely available datasets from the repository
of GEE; MODIS satellite terra sensor evapotranspiration
(ET), and potential evapotranspiration (PET) 8-Day Global
500m (Mu etal. 2007) and MODIS Terra NDVI 8-Day
Global 500m (Didan etal. 2015). In GEE, ET and PET data-
set are available from January 2001 to the present, while the
NDVI availability date is from February 2000 to the present.
So in this study, DSI mapping and monitoring is done from
2001 to 2019. MODIS derives the product for evapotranspi-
ration through Penman–Monteith equation-based algorithm.
The input data for the algorithm is the summation of nightly
and daytime daily evapotranspiration and vegetation data
with a temporal resolution of 8days (Running etal. 2019).
Evapotranspiration studies the ecosystem, which in exten-
sion analyzes the carbon, water, and energy cycle. The ratio
of ET to PET determines the available water in a terrestrial
environment and helps study dry seasons in a region (Mu
etal. 2012). The agriculture sector is the one most affected
by droughts at the beginning of dry season, and almost 18%
of the global population is employed in this sector (Meza,
etal. 2019).
2.2 Methodology
In this study, MODIS terra data is used for analysis and
computation. The parameters used in this index are evapo-
transpiration (ET), potential evapotranspiration (PET), and
normalized difference vegetation index (NDVI).
Before computing DSI, the preprocessing performed on
datasets is cloud percent reduction, shadows removal, filter-
ing timeline of our study, rescaling values to monthly data,
and masking vegetation data.
The methodology for computation is as follows: first, the
ratio between evapotranspiration and potential evapotran-
spiration is computed. In the second step, the ratio is stand-
ardized. Thirdly, the precomputed product of NDVI from
MODIS is standardized. Fourthly, the sum of both stand-
ardized values is calculated. In the final step, the sum from
the previous step is standardized, and that value is named
drought severity index or DSI.
The equations of the study are:
In the above equation, Tran denotes the symbol for the
ratio between the input variables. ET means evapotranspira-
tion, and PET symbolizes potential evapotranspiration.
In Eq.2, Z1 represents the standardizing ratio for the
transpiration ratio calculated in Eq.1.Trani means transpira-
tion ratio of the specified 8-day period,
Tra n mean
transpiration mean/average, and
Tra n SD
indicates the value
of the standard deviation for the transpiration ratio.
In Eq.3, Z2 represents a symbol of the standardizing
ratio for the NDVI values.
indicates NDVI value of
the specified 8-day period,
Tra n mean
symbolizes NDVI mean/
average, and
Tra n SD
indicates the value of the standard devia-
tion for the NDVI value of a specified period.
Z represents the sum of standardized values from Eqs.2
and 3.
Tra n
Tra n
Tra n
Tra n
412 R. Khan, H. Gilani
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
In Eq.5, DSI denotes the drought severity index, and Z
values standardize ratio from Eq.4.
is the value of the
specified period,
symbolize mean/average, and
indicates the value of the standard deviation for the Z value
of a specified period.
3 Results
For spatial maps, the statistical median is computed for the
final product for the yearly result. The reason for not taking
mean instead of the median is that the results are shown
more clearly. A visual aid, for this reason, is shown in Fig.1
with 2001 as an example. Four areas are circled in the Fig.1
that shows the extreme droughts visible only with statistical
The range chosen is from 1 to + 1, which means
droughts will have values close to 1, and wet zones will
have values near
1. The results discussed will mainly
include extreme cases.
The maps are read in close detail on the GEE map by
zooming in and slightly reducing the opacity to observe the
boundaries of countries. The maps shown in Fig.2 are at
100% opacity and zoomed out to cover the whole globe. The
results are discussed in detail below.
3.1 North America
3.1.1 2001
More than half of Mexico is going through an extremely dry
season. In the USA, Texas, Oklahoma, and California show
dark red patches of extreme drought; North Carolina, South
Carolina, Georgia, San Francisco, and Florida have mild and
severe droughts in certain areas. The Dominican Republic
has extreme and mild dry situations in certain areas.
3.1.2 2002
Arizona, California, Texas near the Mexico border, and
southern Nevada in the USA are facing extreme dry seasons.
Kansas and Colorado are facing mild to severe drought.
Mexico has an extreme drought in certain parts, and severe
and mild in others. Quebec and Ontario in Canada are dis-
playing red patches of extreme and severe dry situations.
3.1.3 2003
In the USA, Texas is facing extreme drought; California and
New Mexico have mixed severe and extreme dry situations.
Mexico is going through an extreme and severe dry period
near the USA border.
3.1.4 2004
Cuba is facing extreme dryness while Arizona and the neigh-
boring areas are going through mild and severe dry periods.
3.1.5 2005
The only notable dry patch in the whole continent is in the
middle of Mexico that ranges from mild to severe.
Fig. 1 Right side map shows the mean value of twelve months, and the left side map is the statistical median for 2001
413Global drought monitoring with drought severity index (DSI) using Google Earth Engine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
Fig. 2 a Spatial maps of DSI from 2001 to 2015. b Spatial maps of DSI from 2016 to 2019
414 R. Khan, H. Gilani
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
3.1.6 2006
Mild to extreme dry situation found in Mexico towards
the north and in Texas and Oklahoma in the USA. Mild
dryness can also be seen towards California and Arizona
in the USA.
3.1.7 2007
Arizona and California in the USA and Baja California in
Mexico are showing cases of severe to extreme droughts.
3.1.8 2008
Mild to a greater extent and severe in small areas a drought
season could be noticed in the whole Mexico and Texas,
Arizona, California, and Nevada in the USA.
3.1.9 2009
Texas has severe to extreme drought. The states of Nevada,
California, Arizona, and New Mexico in the USA are going
through cases of mild to severe droughts. Mexico has also
spots of severe and mild dryness near the USA border.
3.1.10 2010
Mild to slightly severe dryness in a very small area is
noticed towards the east of the USA.
3.1.11 2011
This year shows the worst case of the extreme dry season
that spans over Texas, the majority of New Mexico and
Oklahoma, and some parts of Arizona and Kansas. Mexico
also seems to be suffering from an extreme drought that
has reached almost every part of the country.
3.1.12 2012
In the USA, Texas and New Mexico are displaying evi-
dence of mild to severe and slightly extreme dry season.
Mexico has a large patch of extreme drought.
3.1.13 2013
The state of California in the USA is extremely dry this
year. Colorado, Texas, and New Mexico in the USA are
Fig. 2 (continued)
415Global drought monitoring with drought severity index (DSI) using Google Earth Engine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
ranging in dryness from mild to extreme. The country of
Mexico has also mild to the severe dry season.
3.1.14 2014
In the USA, the states of Texas, California, and a small part
of Nevada towards the south are in extreme drought. Mexico
has recovered slightly from the last 3years’ worst drought
but there are still signs of mild and severe dryness in parts of
the country, especially the ones near the USA border.
3.1.15 2015
California state in the USA and the Gulf of California in
Mexico are the only regions with mild and extreme dryness
cases. The rest of the continent is exceptionally in a better
state this year.
3.1.16 2016
The same situation is noticed this year as 2015, with the only
exception of drought reducing to the south of the state of
California and a mild dry period in Arizona State.
3.1.17 2017
The states of Wyoming, Idaho, and California are the only
regions in the continent this year to show small signs of mild
and severe dryness.
3.1.18 2018
The provinces of Quebec, Ontario, and Newfoundland and
Labrador in Canada are extremely dry, and in the USA, the
regions of Texas, California, and Arizona are displaying
severe and extreme dryness this year.
3.1.19 2019
Newfoundland and Labrador are severely dry, while in
the USA, the dryness this year is in the form of very small
patches of extreme cases that are found in the states of Flor-
ida, Montana, California, and Wyoming.
3.2 South America
3.2.1 2001
Extreme dry situation is observed towards northeast Bra-
zil, northern parts of Venezuela, a very small portion of the
state of Rio de Janeiro in Brazil, towards Los Angeles and
Concepcion in Chile, and San Juan and Perito Moreno in
3.2.2 2002
A very small region in Paraguay and Bolivia, a vast area
of Santa Rosa in Argentina, and almost half of the state
of Mato Grosso Do Sul and parts of state of Sao Paolo in
Brazil are going through mild and severe dryness.
3.2.3 2003
Argentina is particularly dry during this year. Brazil is fac-
ing severe and mild dryness throughout the country near
the South Atlantic Ocean.
3.2.4 2004
Peru and Bolivia, a small area of Argentina, and the state
of Rio Grande Do Sul in Brazil are facing mild and severe
3.2.5 2005
Peru shows extreme cases of drought while northeast
Brazil and Argentina are mildly dry. Amazonia faced dry
season as well.
3.2.6 2006
Argentina is the only country displaying mild to extreme
dryness. Mild dryness is seen towards Peru and northeast
3.2.7 2007
Mild and severe dry situation dominates parts of Brazil,
Paraguay, and Peru. Chile is only mildly dry and in a small
portion. Argentina is displaying severe to extreme dryness
in the middle.
3.2.8 2008
Argentina and Uruguay are exhibiting extreme conditions of
dryness that span over a large extent of the respective coun-
tries. Bolivia and Peru are displaying mild to severe droughts.
Brazil in the southeast and Chile are only mildly dry.
3.2.9 2009
A large portion of Argentina is going through extreme dry-
ness. Paraguay, Bolivia, and the state of Rio Grande Do Sul
of Brazil are showing mild dry seasons. The otherwise warm
and dry northeast Brazil is exceptionally wet this year.
416 R. Khan, H. Gilani
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
3.2.10 2010
Extreme droughts are noticed in a vast area of Argentina, Para-
guay, and Bolivia. Uruguay, Peru, and South Brazil have mild
cases of dryness.
3.2.11 2011
Smack in the middle of Argentina is a massive dark patch
of extreme drought, and towards the east of the country, the
drought ranges from mild to severe. Santa Cruz de la Sierra of
Bolivia also has evidence of extreme drought.
3.2.12 2012
Northeast Brazil and the west of Argentina have huge evidence
of extreme dry season this year.
3.2.13 2013
Northeast Brazil has extreme drought this year too. The
drought in Argentina has not reduced its intensity but it has
shifted to the north, where it is even spread to Paraguay and the
middle of the country. Chile is also exhibiting mild dryness.
3.2.14 2014
An almost no notable portion of northeast Brazil, north Peru,
and south of Chile has severe and mild dry seasons.
3.2.15 2015
Northeast Brazil has a severe and extreme drought. South
of Chile and north of Venezuela near the Caribbean Sea are
also extremely dry. Argentina is mildly dry in certain parts.
3.2.16 2016
Northeast Brazil and south of Argentina are mild to
extremely dry while Bolivia and Peru are mild to severely
dr y.
3.2.17 2017
The only case of drought this year is seen towards northeast
Brazil, and it is mild to extreme.
3.2.18 2018
Drought of northeast Brazil has reduced from extreme
to slightly severe, and a mild case of dryness has also
emerged in a small area of Argentina.
3.2.19 2019
Chile has an extreme case of dryness, and Argentina has
mild and severe small dry patches in the middle of the coun-
try. In the heart of the continent, Pantanal is also suffering
from the dry season.
3.3 Africa
3.3.1 2001
The only vegetative parts lie near the river banks in Tuni-
sia and Algeria, and they are in extreme dry situations.
Morocco as a whole is going through a dry season. Soma-
lia and the parts of Ethiopia that share a border with Soma-
lia and Madagascar are in extreme and mild dry season
periods in one part or the other.
3.3.2 2002
Severe and extreme situations are observed in Namibia, north-
ern parts of South Africa, a small area in Zambia towards the
south, and Botswana. Western Kenya, Tunisia, Algeria, and
north eastern Uganda are facing extreme drought.
3.3.3 2003
Botswana and Namibia, are gone through extreme dry
periods. South Africa is facing mild and extreme droughts
throughout the country. A small part of Algeria and
Morocco are facing severe dryness.
3.3.4 2004
Botswana and South Africa are having extreme dry sea-
sons while Namibia is only mildly dry.
3.3.5 2005
Ranging from extreme to severe droughts are noticed in
Zambia, South Africa, Tanzania, Botswana, Morocco,
Namibia, and Angola. Algeria, Mozambique, and Kenya
show mild dry seasons.
3.3.6 2006
Algeria has a small severe dry patch while Kenya, Ethio-
pia, Madagascar, and Tanzania have mild dryness.
417Global drought monitoring with drought severity index (DSI) using Google Earth Engine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
3.3.7 2007
Morocco is extremely dry while Zimbabwe, South Africa,
Botswana, Namibia, and southern Angora are ranging in
dryness from mild to severe and extreme.
3.3.8 2008
Algeria and Morocco are severe to extremely dry. Somalia,
Ethiopia, Zimbabwe, Mozambique, and Botswana are exhib-
iting mild to severe dryness. South Africa and Namibia are
mildly dry and only in certain parts.
3.3.9 2009
A small part of South Africa that lies to the south of the
country and all of Kenya are displaying extreme droughts.
Somalia and Ethiopia are showing mild and slightly severe
3.3.10 2010
Somalia and two separate regions in South Africa, one to
the north and the other to the south, display clear signs of
extreme dryness. South of Madagascar also has some signs
of severe drought.
3.3.11 2011
Somalia, Ethiopia, and Kenya are enduring extreme droughts
this year. A tiny region in Sudan shows severe dryness
while the rest of the country is mildly dry. South Africa and
Namibia are exceptionally wet this year.
3.3.12 2012
Zimbabwe, south of Mozambique, and Botswana exhibit
signs of dryness that range from mild to extreme. There
is a small straight line of severe drought passing through
Ethiopia. West of Angola and southeast of Kenya are also
mildly dry.
3.3.13 2013
Botswana, northeast Namibia, the center of Zimbabwe and
South Africa, and southwest Angola are mild to extremely
dr y.
3.3.14 2014
There are no severe or extreme droughts this year. The whole
continent is mostly in no drought situation or mildly dry.
3.3.15 2015
Ethiopia, Kenya, Tanzania, Zimbabwe, and Mozambique
are mildly dry, while the majority of South Africa, south
of Angola, north of Namibia, and regions of Botswana near
the South African border are facing extreme dry seasons.
3.3.16 2016
The most extreme drought noticed on the map lies to the
south of Mozambique in the form of large and dark patches
of red. Areas near the shared border of South Africa and Bot-
swana are experiencing mild to extreme dryness. Namibia
has a moderately severe case of drought that covers most of
the country’s north side. Tunisia, Morocco, Algeria, and the
rest of South Africa are mildly dry.
3.3.17 2017
Extreme cases of dryness are noticed to the east of the
continent in Somalia, Kenya, and Tanzania; to the north in
Morocco, Algeria, and Tunisia; and towards the south in the
South Africa region. Madagascar and Ethiopia are mildly dry.
3.3.18 2018
Southern regions of both Madagascar and South Africa are
extremely dry. Namibia, Botswana, and Guinea are mildly
dr y.
3.3.19 2019
South Africa has the worst case of extreme dryness that
extends to more than half of the country. The vast area of
Namibia is severely and extremely dry while small regions
of Mozambique and Angola are mild to severely dry. Soma-
lia, Kenya, Zambia, and Morocco are mildly dry.
3.4 Europe
3.4.1 2001
Extreme droughts are observed in Greece, northeast and
southeast parts of Spain, more than half Italy, southern parts
in Albania, a very small part in Romania and Bulgaria near
the black sea, and western Turkey. Madrid in Spain is going
through a mixture of the mild and severe dry season.
3.4.2 2002
The only red patch this year in Europe is found in Italy
towards the south and in Zaragoza in Spain.
418 R. Khan, H. Gilani
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
3.4.3 2003
France, Italy, and the UK are facing severe and extremely
dry seasons in certain parts.
3.4.4 2004
No extreme or severe situation was noticed in Europe this year.
3.4.5 2005
Portugal and Spain are facing extreme and severe drought
situations throughout the whole region. The rest of the con-
tinent shows no anomalies.
3.4.6 2006
Spain shows mild to severe dry periods throughout the coun-
try while France has mild dryness towards the areas near the
bank of the Balearic Sea.
3.4.7 2007
Spain towards Barcelona, France in Monaco and neigh-
boring areas, Italy in the middle, and Albania suffer from
mild to extreme dryness. Greece and west Turkey are going
through mild to severe dryness situations.
3.4.8 2008
Mildly dry situation dominates this year for many European
countries. These countries are Italy, Portugal, Spain, Greece,
Croatia, west Turkey, and France.
3.4.9 2009
Spain, Portugal, and France are mild to extremely dry while
Italy, UK, and Ukraine are mild to moderately severe dry.
3.4.10 2010
The dryness noticed this year is somewhere between mild
and severe in the UK, France, and Austria.
3.4.11 2011
Italy is slightly severe dry while the rest of the countries
show no signs of harmful droughts.
3.4.12 2012
A mild dryness is noticed towards Romania, France, Spain,
and the south of Ukraine.
3.4.13 2013
The UK and small regions in France and Germany are mildly dry.
3.4.14 2014
Towards southeast Spain, there is an extreme drought
3.4.15 2015
Only mild dryness is noticed in Spain.
3.4.16 2016
Spain has a slightly severe to an extreme case of dryness
towards its southeast.
3.4.17 2017
Portugal and especially Spain are severe to extremely dry,
while Sardinia in Italy is mild to extremely dry.
3.4.18 2018
A small patch of land in Portugal is extremely dry. France
has mild to severe cases of dryness in the middle. European
Russia has evidence of mild dryness in a small area.
3.4.19 2019
Spain towards the southwest and Portugal towards the south-
east are mild to severely dry. A small region in France, Ger-
many, and Austria is suffering from a mild case of dryness.
3.5 Asia
3.5.1 2001
More than half of Pakistan, almost all vegetation in Turk-
menistan, vast areas of Afghanistan and Uzbekistan, parts
of Kazakhstan near the Caspian Sea, and Shanxi, Ningxia,
Henan, and Inner Mongolia in China are going through
extreme dry situations. The rest of China, almost the
whole of India, Jordan, Syria, and Lebanon are displaying
a combination of mild and severe dry situations.
3.5.2 2002
Pakistan in the majority, parts of Azerbaijan, and
Rajasthan of India face extreme drought. China is going
through a mild dry season.
419Global drought monitoring with drought severity index (DSI) using Google Earth Engine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
3.5.3 2003
China is mildly dry. Pakistan in the middle and India
towards the south are facing extreme drought.
3.5.4 2004
Pakistan is facing extreme drought while China, Thailand,
and India are mildly dry.
3.5.5 2005
India and China are going through dry periods that range from
mild to severe in certain parts. Thailand and Vietnam show
extreme to severe drought conditions, while Cambodia is mildly
dr y.
3.5.6 2006
Mild drought in the majority of China, Turkmenistan, and
India, while severe in very small areas of the same coun-
tries, are the dominant droughts this year in Asia. Uzbeki-
stan and Pakistan have mild droughts.
3.5.7 2007
Mild to severe droughts are found towards east Turkey,
Cyprus, Turkmenistan, Uzbekistan, and India. Syria and
China are mildly dry.
3.5.8 2008
A large number of countries are showing severe and extreme
droughts this year. The Republic of Cyprus, Turkey towards
the southeast, Turkmenistan, Afghanistan, and Iraq have
dryness ranging from severe to extreme. Uzbekistan, Syria,
Tajikistan, Iran, Yemen, and Saudi Arabia have drought
situations ranging from mild to extreme. Severe and mild
droughts are also seen in China and Indonesia.
3.5.9 2009
Iraq, Iran, and Yemen are severe to extremely dry. China,
Pakistan, Turkmenistan, and India are having dry seasons
in the range of mild to slightly severe.
3.5.10 2010
Patches of extreme and severe droughts are seen in Syria,
Iran, Iraq, Turkey, Lebanon, Israel, Turkmenistan, and the
southeastern corner of Russia. Those with mild dryness
are China, India, Myanmar, Thailand, Uzbekistan, and
3.5.11 2011
A minimal area in the southeast corner of Pakistan, vast
regions of Uzbekistan, and Turkmenistan are extremely
dry while China, Tajikistan, Iran, and Iraq have mild dry
3.5.12 2012
China has evidence of severe dryness in more than one prov-
ince. India and Turkmenistan have specific areas that are dry
in the range of somewhere between mild and severe. In Paki-
stan, extreme cases of dryness are seen in Potohar and Sindh.
3.5.13 2013
Mild to severe dryness is seen towards Qinghai, Tibet,
and Henan provinces of China, and also towards Syria
and neighboring countries, and towards the south of India.
Aktau Aktay of Kazakhstan and west of Turkmenistan are
extremely dry.
3.5.14 2014
Southern corner of India, vast areas of Turkmenistan and
Azerbaijan, and a small area towards the west of Afghanistan
are in extreme and severe drought situations.
3.5.15 2015
Only mild dryness is noticed this year and is found in China,
Pakistan, Iran, Iraq, and Syria.
3.5.16 2016
Small regions in Afghanistan, Uzbekistan, Iran, Syria, and
neighboring countries, and Thailand have patches of extreme
dryness. The areas near the shared border of Mongolia and
Russia on both sides are severely dry. Turkmenistan, Paki-
stan, and India have evidence of mild dryness in certain
420 R. Khan, H. Gilani
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
3.5.17 2017
Syria, Iraq, and the south of India have evidence of mild to
severe dryness. China is mildly dry.
3.5.18 2018
Afghanistan, Uzbekistan, Turkmenistan, Iran, and Pakistan
show extreme patches of dryness.
3.5.19 2019
Extreme dryness is found in China, India, Azerbaijan, and
Cambodia. Turkmenistan, Pakistan, Iran, and Yemen are
exceptionally wet this year.
3.6 Australia
3.6.1 2001
Southeastern parts of New Zealand are in the claws of
extreme drought. Australia has a combination of mild and
severe drought throughout South Australia, West Aus-
tralia, and Queensland.
3.6.2 2002
Except for a small portion in the middle of Australia, the
whole country is in the clutches of the severe and extreme
dry season.
3.6.3 2003
Severe and extreme dry situation is noticed towards the
south of New Zealand and the whole of Australia.
3.6.4 2004
Mild and extreme dryness is noticed towards Queensland
and New South Wales. South Australia and Northern Ter-
ritory are going through a severe and mild drought.
3.6.5 2005
Northern Territory and Western Australia have extreme
dryness, Queensland and New South Wales display severe
to extreme dryness, and South Australia shows mild to
severe dry patches.
3.6.6 2006
Extreme and severe drought spans over the whole area of
Queensland and New South Wales, while the Northern Ter-
ritory and South Australia are having almost the same inten-
sity of drought but limited to certain zones.
3.6.7 2007
Towards the north, the drought is mild, but going towards
the south makes the drought mild to extreme. Even the oth-
erwise cold region of Tasmania is going through a severe
dry phase. New Zealand is also displaying severe and mild
dryness in certain areas.
3.6.8 2008
Queensland displays less severe drought than the rest of
Australia, where extreme and severe droughts dominate the
3.6.9 2009
Mild to severe dryness is spanning over almost the whole
country of Australia except for Tasmania.
3.6.10 2010
This year, extreme drought is confined only towards Western
Australia. New Zealand is mildly dry in certain areas. It is
also a good point to notice that the majority of Australia is
extremely wet this year.
3.6.11 2011
Mild to slightly extreme dryness is noted only in the south-
west corner of Western Australia. The rest of the continent
is exceptionally wet.
3.6.12 2012
Dryness noticed this year is relatively in a small area and is
mostly mild and extreme in very small patches.
3.6.13 2013
New South Wales and far west of Western Australia are the
only areas with mild dryness. The rest of the territories of
mainland Australia have severe to extreme droughts.
421Global drought monitoring with drought severity index (DSI) using Google Earth Engine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
3.6.14 2014
Mild to extreme droughts are noticed all over mainland Aus-
tralia with moderate patches of wet areas in between too. All
in all, the situation is dire but not as intense as some years.
3.6.15 2015
Once again, droughts ranging from mild to extreme are com-
mon this year in mainland Australia. Cheviot and neighbor-
ing areas in New Zealand near the back of River Tasman
have a bright red patch of extreme dryness.
3.6.16 2016
Extreme dryness patch noticed this year is relatively small
and is found in the northwest part of Western Australia. The
rest of the mainland has mild dryness and no droughts at all.
3.6.17 2017
No notable dryness is noticed this year.
3.6.18 2018
South Australia and Western Australia have mild to severe
dry seasons while the rest of the territories of mainland Aus-
tralia are exhibiting mild to extreme dry periods.
3.6.19 2019
Worst case of dryness is noticed this year that ranges from
severe to extreme and is covering almost the whole of main-
land Australia.
4 Discussion
Two countries in North America are noticed in “Sect.3
with regards to constant droughts: Mexico and the USA.
Mexico has a warm and dry climate towards the north and
annual rainfall of fewer than 100mm, while towards the
south of the country, the climate is wet tropical, and the
annual rainfall exceeds beyond 3000 mm (Méndez and
Magaña 2010). The USA has been the focus of droughts
and other natural disasters for a long time (Kogan 1997),
and it has been observed through many research studies that
perhaps greenhouse gasses and anthropogenic activities con-
tribute a lot towards the recurring droughts in states like
California (Folger and Cody 2015). Research has proven
that droughts towards the southeast of the USA mostly occur
due to the dwindling soil moisture and extreme heat (Xu
etal. 2018).
In 2003, a constant wave of dry season was noticed
towards northeastern Mexico and the western side of the
USA (Levinson and Waple 2004). In 2005, the droughts were
limited to the center of the USA, while towards the southwest
and northeast, severe precipitation was recorded (Shein
2006). Mexico had a warmer season in 2006, and the interior
of the USA went through an intense dry season. This year was
one of the two warmest ones for the USA and Canada. Also,
the same year observed El Nino phenomena in the central
USA and the Caribbean towards the end (Arguez 2007).
Noticeable drought was detected in the USA in 2008 due to
the dry period from October to June towards southern Texas
and extremely low precipitation from December to June in
south-central Texas. Mexico suffered several causalities due
to the severe droughts and then the wildfires that followed
soon after (Peterson etal. 2009). Texas and Hawaii suggested
agriculture loss due to drought in 2009. California and the
state of Alberta also suffered from an intense dry season
in 2009. Mexico suffered from two droughts in 2009; the
first one appeared in March and April, causing loss to the
economy and livestock, while the second intense drought
hit the country after June due to the appearance of El Nino
(Arndt etal. 2010). Droughts first originated in the southwest
in 2011 and spread to Central Plains and then southeast in the
USA. Palmer hydrological index noted a record of low-level
precipitation in the history of 117years in major parts of
the USA. Extreme to severe drought in 2011 in Mexico was
one of the worst cases of the dry season that covered 85% of
the area of the country in June and caused huge loss to the
economy and livelihood of the residents (Arndt and Blunden
2012). More than half of Mexico was declared under drought
in May of 2013 (Jessica Blunden and Arndt 2014). California
was under threat of intense drought during the whole year of
2015 (Jessica Blunden and Arndt 2016). Droughts developed
towards northeast and southeast in the USA in 2016, while
the previous droughts towards the West and Great Plains
lessened in intensity due to the high precipitation (Jessica
Blunden and Arndt 2017). Drought took a heavy toll on USA
in 2017 with one quarter of the country going through the
dry season by the end of the year. Drought is noticed towards
the south of Mexico due to high temperatures than usual
and less precipitation in 2017 (Jessica Blunden etal. 2018).
Oregon and a large portion of the southwest faced severe
and extreme droughts for the whole year of 2018. Southern
Mexico’s severe dry season resulted in water shortage and
a negative impact on pastures in 2018 (Jessica Blunden and
Arndt 2019).
From our results, we have observed that droughts in Mex-
ico are mostly observed of extreme nature and are more than
often occurring towards the north of the country. It is also
observed that droughts in Mexico and southern states of the
USA occur in most cases at the same time and of the same
intensity. Our study points out that in the current century the
422 R. Khan, H. Gilani
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
areas most affected by extreme and severe droughts in the
USA are the states of Texas, California, Nevada, and Ari-
zona. Other states such as Florida, New Mexico, and Kansas
also suffer from frequent droughts that range from mild to
severe in intensity.
The results of our study point towards recurring and
extremely dry seasons towards Brazil and Argentina. Our
studies also regard some of mild to severe droughts towards
Peru, Chile, and Venezuela.
Western Amazonia suffered the driest season in the
history of 40years in 2005 (Shein 2006). Modulation due
to Madden–Julian oscillation was observed in the warmer
than usual year 2006 throughout the continent (Arguez
2007). The grassland and steppe’s of Argentina, Uruguay,
Paraguay, and southern Brazil faced intense droughts in
2008 due to the 60 to 80% less than normal precipitation
(Peterson etal. 2009). Argentina, Uruguay, Paraguay, and
southern Brazil again suffered from droughts in 2010 and
experienced shortage of water in the hydropower stations and
also the supply of water to the summer crops was impacted
(Arndt etal. 2010). Amazon faced a major drought in 2010
that started during the El Nino and increased in intensity
during the La Nina (J. Blunden etal. 2011). Northeast Brazil
experienced an intense dry season despite the record of above
average precipitation in other parts of the country in 2012
(J. Blunden etal. 2013). The drought from previous year
extended to 2013 and caused huge losses to agriculture in
northeast Brazil (Jessica Blunden and Arndt 2014). Colombia
and Venezuela suffered from dry season for almost the whole
year in 2015. Northeast and southeastern Brazil still suffered
from intense droughts that started in previous years (Jessica
Blunden and Arndt 2016). Brazil and Bolivia faced droughts
in 2016 due to drier than usual weather that lasted most of the
year. Amazon also went through a dry season due to El Nino
(Jessica Blunden and Arndt 2017). The drought continuing
from previous years took a turn for the worst in 2017 in west-
central Brazil and made it the worst case of drought in the
last 57years. Chile and Argentina suffered from wildfires due
to the drought that erupted at the beginning of 2017 (Jessica
Blunden etal. 2018). 2018 brought severe to extreme dry
season in northeastern Brazil and Paraguay. The La Nina
was weak in 2018, resulting in one of the worst droughts
in 50years in Argentina (Jessica Blunden and Arndt 2019).
Argentina is the largest crop yielding country of South
America, making it essential agriculture-wise (Kogan 1997).
The impacts of droughts on the agriculture sector are of
particular interest because it heavily depends on the water,
which is a basic need for survival for all living beings, and
also a hit to agriculture has caused massive famines in the
past (Meza, etal. 2019). Northeast Brazil has 95% farm-
land dependent on rain and adds that a high percentage of
its inhabitants live in poverty, so naturally, the impacts of
droughts on this region are severe (Cunha etal. 2018). The
increased dry seasons in Brazil have made policymakers,
scientists, and the government worried over its impacts on
the socio-economic, food, and the fast-shifting of some of
the lands towards desertification (Marengo etal. 2017).
The results of our study indicate that in the continent
Africa the worst cases of droughts are noticed towards East-
ern Africa, Southern Africa, and the northern parts. In the
center of Africa, the situation is not that dire and there are
almost no signs of extreme to severe dry seasons. Towards
its southern parts, the island of Madagascar also suffers
from time to time due to severe dryness. In Eastern Africa,
the countries suffering more frequently and the most are
Ethiopia and Somalia. Kenya also goes through extreme dry
seasons but not as much as the other two countries. South
Africa suffers from extreme droughts but other countries
of south like Namibia, Botswana, Zimbabwe, Mozambique,
and Angola also go through severe and extreme dryness fre-
quently. Towards the north, Morocco, Algeria, and Tunisia
went through extreme droughts at the beginning of the cen-
tury and then through severe and mild dry seasons for the
rest of the vast majority of the century.
African countries are prone to droughts and have already
pushed many countries towards desertification and its impact
on agriculture has raised food insecurity in the inhabitants
(Eckstein etal. 2020). Meza etal. (2019) concluded in their
research that the ten countries most vulnerable to droughts
are Zimbabwe, Namibia, Botswana, Morocco, Kosovo,
East Timor, Mauritania, Lesotho, Kazakhstan, and Algeria.
Seven out of ten of the aforementioned countries belong to
the African continent. According to the Fourth Assessment
Report (AR4) of the Intergovernmental Panel on Climate
Change (IPCC), there is a high probability of reduced annual
rainfall towards Northern Sahara Africa and Mediterranean
Africa. There is also a high chance of reduced rainfall in the
winter season towards South Africa and a slight increase in
annual mean of precipitation towards Eastern Africa (Knox
etal. 2012). Studies indicate that anthropogenic activities
in the Indian Ocean caused the warming of the ocean which
reduced the precipitation rate by 15% in the crop growing
seasons towards the eastern and southern Africa, and as a
result, the crop yield is severely impacted all across sub-
Saharan Africa (Conway etal., 2011).
Ethiopia has faced some intense mega droughts and suf-
fered the consequences. The drought in Ethiopia in 2009
alone affected six million people and the country had to rely
on foreign aid to deal with the aftermaths (Bayissa etal.
2018). Similarly, in 2015–2016, an El Nino event triggered
an extreme drought towards eastern Africa that affected ten
million inhabitants of Ethiopia, and once again the issue of
food insecurity was risen (Delbiso etal. 2017). Evidence
is available that suggests the cause of the 2015 drought in
South Africa was due to sea surface temperature change
which leads to high atmospheric temperature and less
423Global drought monitoring with drought severity index (DSI) using Google Earth Engine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
rainfall (Eckstein etal. 2020). In 2003, droughts prevailed
towards the southern Africa and certain regions of Greater
Horn (Levinson and Waple 2004). Persistent dry season
was noticed in the year 2005 over majority of Greater Horn
throughout the year 2005 while in the same year, towards
the southern Africa the precipitation was below normal dur-
ing the beginning but gradually returned to normal (Shein
2006). Eastern Africa faced a worst case of drought in a dec-
ade during 2011. The dryness was caused by below average
rainfall that resulted mostly from La Nina. Somalia, Kenya,
and Ethiopia were majorly impacted by this drought (Arndt
and Blunden 2012). The drought in 2014 in South Africa
was deemed a worst case of dry season in 80years span.
From May to October in 2014, the island of Madagascar also
suffered from severe drought season (Jessica Blunden and
Arndt 2014). Ethiopia faced worst case of drought in a dec-
ade in 2015 due to El Nino. In 2015, Somalia, Malawi, Zim-
babwe, and Kenya also suffered severe dry seasons (Jessica
Blunden and Arndt 2016). Central and certain northeastern
parts experienced dry season in Africa in 2016 due to below
average precipitation (Jessica Blunden and Arndt 2017).
Crops across the South Africa were severely impacted due
to severe drought in 2018 (Jessica Blunden and Arndt 2019).
For Europe, the results in this study conclude that extreme
and severe dry seasons are not that common when compared
to the rest of the continents. The countries that show signs
of extreme dryness for more than once in the recent century
were Spain, Portugal, France, and Italy. Other countries were
either very small to notice on the map or were suffering from
mild dry seasons and not condemning its inhabitants to food
insecurity. The other notable point in the study was the coin-
cidence of droughts occurring more than once at the same
time in Portugal and Spain. France also joined sometimes in
the dryness when these two countries were suffering but not
that often. Italy showed signs of extreme dryness that were
noteworthy on more than one occasion and that spread to a
larger portion of the country.
2005 was above normal in the eastern Europe and below
normal towards the western parts with respect to precipi-
tation. UK was exceptionally warm while Iberian Penin-
sula suffered a severe drought (Shein 2006). Europe was
warmer than usual in the year 2007 with records of heat
waves in the summer and autumn as well (Arguez 2007).
Iberia, Spain, and Portugal suffered from intense droughts in
2008 (Peterson etal. 2009). In the summer of 2011, drought
was observed towards the eastern parts of England. In the
same year, dry season in the spring in western and central
England became cause for wildfires. Austria, Bulgaria, and
Romania suffered from forest fires due to drought in the
autumn season (Arndt and Blunden 2012). Slovenia faced
extreme drought in 2013 (Jessica Blunden and Arndt 2014).
In 2015, from the southwestern Iberia towards the eastern
Europe, cases of droughts were noted after the long-lasting
heat waves and rainfall deficiency. Several wildfires also
erupted across the Europe due to constant drought and heat
(Jessica Blunden and Arndt 2016). Iberian Peninsula and
France went through drought seasons due to 20% below
average precipitation (Jessica Blunden and Arndt 2017). A
record breaking high temperature was noticed across Italy
and Turkey that resulted in heatwaves and severe drought
season in 2017. Spain and Portugal also faced severe to
extreme droughts in 2017 with the year declared as second
driest for Spain and third driest for Portugal since 1965.
Portugal suffered heavy losses due to the wildfires emerg-
ing due to dry season (Jessica Blunden etal. 2018). Central
and Northern Europe saw record high air temperature that
resulted in droughts across the region (Jessica Blunden and
Arndt 2019).
Climate model studies have predicted that there would be
a significant decline in the precipitation level in the Mediter-
ranean region, and the countries will get affected in the agri-
culture sector due to the dryness (Vicente-Serrano 2007).
However, droughts towards this region have shown no last-
ing impact on the gross primary production of the vegetation
(Vicca etal. 2016). Extreme events in this continent have
impacted biodiversity in the form of endangering of species,
heat waves, migration of animals, and a rise in the carbon
dioxide level (Thuiller etal. 2005).
The results of our study in the case of Asia are suggesting
the vulnerability of certain countries to frequent and extreme
droughts, namely, Pakistan, Kazakhstan, India, China, and
Azerbaijan. Other countries like Afghanistan, Syria, Turk-
menistan, Iran, and Iraq also showcase extreme droughts at
one point or another on more than one occasion. The dry
season in India, Turkmenistan, and China are extreme, but
they seldom cover the vast majority of the country. On the
other hand, Pakistan, Azerbaijan, and Kazakhstan showcase
where more than half of the country’s agriculture is under
the thrall of extreme dry season. Countries in the neighbor-
ing of Syria, like Israel, Lebanon, Jordan, etc., also showed
signs of severe droughts at one point or another.
In 2003, the precipitation returned to near normal that
helped to reduce the long-running droughts of previous years
in southwest Asia, and the same year was slightly warmer for
Russia (Levinson and Waple 2004). In 2005, certain parts of
the continent noted record snowfall and precipitation while
other parts suffered due to the delayed monsoon and above
normal temperature (Shein 2006). January to March in 2008
was a season of severe drought towards northern China, and
the main reason was 30 to 80% less precipitation than usual.
2008 was also severe to extreme droughts in parts of India
and Iran (Peterson etal. 2009). Crops across China were
severely damaged due to the arrival of the intensely dry
season in 2009. In the same year, Pakistan and India went
through one of the worst droughts recorded in history (Arndt
etal. 2010). Russia faced heatwaves and drought-related
424 R. Khan, H. Gilani
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
catastrophes in 2010 (J. Blunden etal. 2011). Sri Lanka went
through one of its worst cases of the dry season in 2012
that hugely impacted the economy (J. Blunden etal. 2013).
China and Pakistan faced dry season of severe intensity in
certain parts in 2013 (Jessica Blunden and Arndt 2014).
Twenty-eight provinces of Iran went through severe drought
conditions in 2016 (Jessica Blunden and Arndt 2017). Iran
and Japan suffered from severe to extreme droughts in 2017
(Jessica Blunden etal. 2018).
Agriculture and livestock are the main sources of
income for more than half the population of people living
in rural areas of South Asia and the recent climate change
is hitting that sector hard (Knox etal. 2012). Similarly,
in South-East Asia, another agriculture-dependent region,
droughts are a frequent occurrence and mostly hit dur-
ing the seasons when the El Nino phenomenon is active
(ESCAP 2019). The region of Kazakhstan has only one-
tenth of its land used for crop sowing while the remaining
is rangeland used for livestock grazing and the repeated
dry seasons do not bode well for either sector (Kogan
1997). Droughts in India also impact agriculture and often
hit the arid and semi-arid parts of the country (Muthuman-
ickam etal. 2011). North China is an important region for
the wheat sown in winter for China and droughts regularly
occur in this part which is a cause of concern for both the
government and the public (Zhang etal., 2016). Pakistan
is prone to droughts and despite being blessed with the
heavy monsoon in summer and western disturbances in the
winter, almost every province of the country has suffered
through a dry season at one point or another (PMD 2018).
In this study, it was noticed that mainland Australia
goes through extreme dry seasons that span over almost
the whole area more frequently. The only years where
there were no signs of severe droughts for the study period
were 2001, 2011, 2012, and 2017. In 2016, there was only
a small area that was affected by extreme dryness while
the rest of mainland Australia was going through good
season precipitation wise. The other notable point in the
study was that Tasmania was the only region of Australia
that seldom saw a dry season that span over a vast area of
the Island. New Zealand was sometimes observed going
through mild and severe dryness but the affected area was
mostly limited to one or other corners of the country.
2003 observed the warmest June in New Zealand and
caused damages across the Australia due to constant heat
and droughts (Levinson and Waple 2004). Despite the fact
that the temperature rebounded in the second half of 2005
in Australia, this year was noticeably warmer that resulted
in severe dry season in the first half (Shein 2006). Aus-
tralia suffered from intense droughts and storms in the year
2006 (Arguez 2007). A prolong dry season in the southeast
of Australia caused shortage of water in the Murray-Dar-
ling basin in 2008 (Peterson etal. 2009). A large number
of area in the New Zealand suffered from soil moisture
deficiency and consequent drought in 2010 (J. Blunden
etal. 2011). 2013 was worst year both intensity wise and
area covered with respect to dry season in New Zealand
(Jessica Blunden and Arndt 2014). The drought in 2014
in Australia led to many bushfires (Jessica Blunden and
Arndt 2015). The extreme and long drought of 2018 in
Australia contributed to a larger extent towards the erup-
tion of fires in 2018/2019 and damaged most of the crops
(Jessica Blunden and Arndt 2019). 2019 brought record
high intensity of droughts in Australia that lead to heavy
bushfires across the country (Jessica Blunden etal. 2020).
Climate model studies have predicted that there will be
a 40% rise in the dry seasons towards Eastern Australia,
and the overall pattern of precipitation will also change, and
so will the temperature rise (Quiggin 2010). Studies have
shown that almost every extreme drought has occurred in
the last 100years due to the El Nino phenomenon and will
continue so in the future (Braganza etal. 2003). A study
published in 2005 pointed out that due to frequent droughts,
in the last 50years, the country’s agriculture has reduced
contribution from 20 to only 5% towards the gross domestic
product (GDP), and the employment rate has also decreased
(Horridge etal. 2005). The droughts destroy fruits and veg-
etable yield in the country, leading to a shortage of supply
for the general public, and once the demand rises, almost
every product spike considerably (Quiggin 2010). According
to the Australian crop report ABARES, the yield of crops
will fall in major areas where the agriculture sector usually
flourishes (ABARES 2019).
5 Conclusion
Droughts are seen with recurring frequency throughout the
globe, with certain parts experiencing more extreme dry sea-
sons than others. Continent-wise, Australia, Africa, and Asia
have the most extreme and frequent drought events while
South America and North America come a close second.
Europe is the least affected by this particular weather event
when compared to other continents. This study declares
countries vulnerable to extreme droughts are: Central
America and southern Mexico in North America; Argen-
tina, northeast Brazil, and southeast Brazil in South Amer-
ica; Italy, Spain, and Portugal in Europe; Ethiopia, Kenya,
Morocco, Tasmania, Zimbabwe, Mozambique, Namibia,
and South Africa in the African Continent; Pakistan, India,
China, Azerbaijan, Iraq, Syria, Iran, and Kazakhstan in Asia;
and the whole mainland Australia. The research performed
in this article can be used to achieve the sustainable develop-
ment goal that demands climate action and mitigation.
425Global drought monitoring with drought severity index (DSI) using Google Earth Engine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
Author contribution Supervision, project administration, writing—
review and editing, and validation were performed by Dr. Hammad
Gilani. Data curation, formal analysis, investigation, methodology,
software, visualization, and writing—original draft were the duty of
Ramla Khan. The main idea of the research was the combined effort
of both authors.
Data availability All the datasets are available in the repository of
Google Earth Engine (GEE). The details and specifications of each
dataset have already been mentioned in “Section2.1.”
The links to each, along with their titles, are mentioned below:
MOD16A2.006: Terra Net Evapotranspiration 8-Day Global 500m:
https:// lpdaac. usgs. gov/ produ cts/ mod16 a2v006/
MODIS Terra Vegetation Indices 16-Day Global 500m: https://
lpdaac. usgs. gov/ produ cts/ mod13 a1v006/
KBDI: Keetch-Byram Drought Index: https:// devel opers. google.
com/ earth- engine/ datas ets/ catal og/ UTOKYO_ WTLAB_ KBDI_ v1
TerraClimate: Monthly Climate and Climatic Water Balance for
Global Terrestrial Surfaces, University of Idaho: https:// devel opers.
google. com/ earth- engine/ datas ets/ catal og/ IDAHO_ EPSCOR_ TERRA
Ethics approval Not applicable.
Consent to participate Not applicable.
Consent for publication Not applicable.
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
ABARES. 2019. “Australian crop report.” Australian crop report, no.
159: 1–28. https:// doi. org/ 10. 25814/ 5c00a 3822b 438
Arguez A (2007) State of the climate in 2006. Bull Am Meteor Soc
88(6 SUPPL.):9–12. https:// doi. org/ 10. 1175/ BAMS- 88-6- State
ofthe Clima te
Arndt, D. S., M. O. Baringer, M. R. Johnson, L. V. Alexander, H. J.
Diamond, R. L. Fogt, J. M. Levy, etal. 2010. “State of the climate
in 2009.” Bull Am Meteorol Soc 91 (7). https:// doi. org/ 10. 1175/
BAMS- 91-7- State ofthe Clima te
Arndt, D. S., and J. Blunden. 2012. “State of the climate in 2011.” Bull
Am Meteorol Soc 93 (7). https:// doi. org/ 10. 1175/ 2012B AMSSt
ateof theCl imate.1
Bayissa Y, Maskey S, Tadesse T, van Andel S, Moges S, van Griensven
A, Solomatine D (2018) Comparison of the performance of six
drought indices in characterizing historical drought for the Upper
Blue Nile Basin, Ethiopia. Geosciences 8(3):81. https:// doi. org/
10. 3390/ geosc ience s8030 081
Blunden J, Arndt DS, Baringer MO (2011) State of the climate in
2010. Bull Am Meteor Soc 92(6):S1-236. https:// doi. org/ 10. 1175/
1520- 0477- 92.6. S1
Blunden J, Berry D, Frajka-Williams E, Holgate S, McCarthy G,
Rayner D, Smeed D (2013) State of the climate in 2012. Bull
Am Meteorol Soc 94 (8 (Supplement)):1–238. https:// doi. org/ 10.
1175/ 2013B AMSSt ateof theCl imate.1/ suppl_ file/ 2013b amsst ateof
thecl imate.2. pdf
Blunden J, Arndt D (2016) State of the climate in 2015. Bull Am Mete-
orol Soc 97(8):Si-S275. https:// doi. org/ 10. 1175/ 2016B AMSSt
ateof theCl imate.2
Blunden J, Arndt DS (2014) State of the climate in 2013. Bull Am
Meteor Soc 95(7):S1-279. https:// doi. org/ 10. 1175/ 2014B AMSSt
ateof theCl imate.1
Blunden J, Arndt DS (2015) State of the climate in 2014. Bull Am
Meteorol Soc 96(7):ES1-32. https:// doi. org/ 10. 1175/ 2015B
AMSSt ateof theCl imate.1
Blunden J, Arndt DS (2017) State of the climate in 2016. Bull Am
Meteorol Soc 98(8):Si-S280. https:// doi. org/ 10. 1175/ 2017B
AMSSt ateof theCl imate.1
Blunden J, Arndt DS (2019) State of the climate in 2018. Bull Am
Meteorol Soc 100(9):Si-S306. https:// doi. org/ 10. 1175/ 2019B
AMSSt ateof theCl imate.1
Blunden J, Arndt DS, Bissolli P, Diamond HJ, Druckenmiller ML,
Dunn RJH, Ganter C etal (2020) State of the climate in 2019.
Bull Am Meteor Soc 101(8):S1-8. https:// doi. org/ 10. 1175/ 2020B
Blunden J, Hartfield G, Arndt DS (2018) State of the climate in 2017.
Bull Am Meteor Soc 99(8):1–330. https:// doi. org/ 10. 1175/ 2018B
Braganza, Karl, Anna Reynolds, David Karoly, and James Risbey.
2003. “Global warming contributes to Australia’s worst drought.
Australas Sci 24 (3): 14–17.http:// cites eerx. ist. psu. edu/ viewd
oc/ downl oad? doi= 10.1. 1. 182. 8155& rep= rep1& type= pdf.
Accessed01 Nov 2019
Conway D, Lisa E, Schipper F (2011) Adaptation to climate change
in Africa: challenges and opportunities identified from Ethiopia.
Glob Environ Chang 21(1):227–237. https:// doi. org/ 10. 1016/j.
gloen vcha. 2010. 07. 013
Cunha AP, M.A., Javier Tomasella, Germano G. Ribeiro-Neto, Mat-
thew Brown, Samia R. Garcia, Sheila B. Brito, and Magog A.
Carvalho. (2018) Changes in the spatial–temporal patterns of
droughts in the Brazilian Northeast. Atmospheric Science Let-
ters 19(10):1–8. https:// doi. org/ 10. 1002/ asl. 855
Delbiso TD, Altare C, Rodriguez-Llanes JM, Doocy S, Guha-Sapir D
(2017) Drought and child mortality: a meta-analysis of small-scale
surveys from Ethiopia. Sci Rep 7(1):2212. https:// doi. org/ 10. 1038/
s41598- 017- 02271-5
Didan, Kamel, Armando Barreto Munoz, Ramon Solano, and Alfredo
Huete (2015) MODIS vegetation index user’s guide (MOD13
series). Vegetation Index and Phenology Lab, The University of
Arizona.https:// vip. arizo na. edu/ docum ents/ MODIS/ MODIS_ VI_
Users Guide_ June_ 2015_ C6. pdf. Accessed1 Nov 2019
Eckstein, David, Vera Künzel, Laura Schäfer, and Malik Winges. 2020.
“Global climate risk index 2020, who suffers most from extreme
weather events? Weather-related loss events in 2018 and 1999 to
2018.” Berlin: Germanwatch, https:// Germa nwatch. Org/ En/ Do wnl
oad/ 7170. Pdf. Berlin: Germanwatch e.V.www. germa nwatch. org/
en/ cri. Accessed 07 May 2021
Elhag KM, Zhang W (2018) Monitoring and assessment of drought
focused on its impact on sorghum yield over Sudan by using
meteorological drought indices for the period 2001–2011. Remote
Sensing 10(8):1231. https:// doi. org/ 10. 3390/ rs100 81231
426 R. Khan, H. Gilani
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
ESCAP, Economic and Social Commission for Asia and the Pacific.
2019. Ready for the dry years. Building resilience to drought
in South-East Asia with a focus on Cambodia, Lao People’s
Democratic Republic, Myanmar and Vietnam.www. asean. org.
Accessed 02 Nov 2019
Folger, Peter, and Betsy A. Cody. 2015. “Drought in the United States:
causes and current understanding.” In Water resources in the
United States: select issues, 45–68.www. crs. gov. Accessed19
Nov 2019
FAO (2019) Proactive approaches to drought preparedness – Where
are we now and where do we go from here? Rome. http:// www.
fao. org/3/ ca579 4en/ ca579 4en. pdf
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R
(2017) Google Earth Engine: planetary-scale geospatial analy-
sis for everyone. Remote Sens Environ 202(December):18–27.
https:// doi. org/ 10. 1016/j. rse. 2017. 06. 031
Horridge M, Madden J, Wittwer G (2005) The impact of the 2002–
2003 drought on Australia. Journal of Policy Modeling 27(3):285–
308. https:// doi. org/ 10. 1016/j. jpolm od. 2005. 01. 008
IDMC. 2020. “Monitoring methodology for displacement associ-
ated with drought.” Geneva.www. inter nal- displ aceme nt. org.
Accessed9 May 2019
IPCC. 2012.“Managing the risks of extreme events and disasters to
advance climate change adaptation: special report of the Intergov-
ernmental Panel on Climate Change.” IPCC. Vol. 9781107025.
Cambridge University Press, Cambridge, UK, and New York, NY,
USA, 582 pp. https:// doi. org/ 10. 1017/ CBO97 81139 177245. 009
Knox J, Hess T, Daccache A, Wheeler T (2012) Climate change
impacts on crop productivity in Africa and South Asia. Environ
Res Lett 7(3):034032. https:// doi. org/ 10. 1088/ 1748- 9326/7/ 3/
Kogan FN (1997) Global drought watch from space. Bull Am Meteor
Soc 78(4):621–636. https:// doi. org/ 10. 1175/ 1520- 0477(1997)
078% 3c0621: GDWFS% 3e2.0. CO;2
Levinson DH, Waple AM (2004) State of the climate in 2003. Bull Am
Meteor Soc 85(6):S1-72. https:// doi. org/ 10. 1175/ 1520- 0477- 85.
6s. s1
Marengo JA, Torres RR, Alves LM (2017) Drought in northeast Bra-
zil—past, present, and future. Theoret Appl Climatol 129(3–
4):1189–1200. https:// doi. org/ 10. 1007/ s00704- 016- 1840-8
Méndez M, Magaña V (2010) Regional aspects of prolonged mete-
orological droughts over Mexico and Central America. J Clim
23(5):1175–1188. https:// doi. org/ 10. 1175/ 2009J CLI30 80.1
Meza I, Michael. Hagenlocher, Gustavo. Naumann, J Vogt, and Janna.
Frischen. (2019) Drought vulnerability indicators for global-scale
drought risk assessments. JRC Technical Reports. https:// doi. org/
10. 2760/ 73844
Meza I, Siebert S, Döll P, Kusche J, Herbert C, Rezaei EE, Nouri H
etal (2019) Global-scale drought risk assessment for agricultural
systems. Natural Hazards and Earth System Sciences Discussions
20(2):1–26. https:// doi. org/ 10. 5194/ nhess- 2019- 255
Mu Q, Heinsch FA, Zhao M, Running SW (2007) Development of a
global evapotranspiration algorithm based on MODIS and global
meteorology data. Remote Sens Environ 111(4):519–536. https://
doi. org/ 10. 1016/j. rse. 2007. 04. 015
Mu Q, Zhao M, Kimball JS, McDowell NG, Running SW (2013)
A remotely sensed global terrestrial drought severity index.
Bull Am Meteor Soc 94(1):83–98. https:// doi. org/ 10. 1175/
BAMS-D- 11- 00213.1
Muthumanickam D, Kannan P, Kumaraperumal R, Natarajan S,
Sivasamy R, Poongodi C (2011) Drought assessment and moni-
toring through remote sensing and GIS in western tracts of Tamil
Nadu, India. Int J Remote Sens 32(18):5157–5176. https:// doi. org/
10. 1080/ 01431 161. 2010. 494642
Peterson, T. C., M. O. Baringer, H. J. Diamond, R. L. Fogt, J. M. Levy,
J. Richter-Menge, P. W. Thorne, L. A. Vincent, and A. B. Watkins.
2009. “State of the climate in 2008.” Bull Am Meteorol Soc 90
(8). https:// doi. org/ 10. 1175/ BAMS- 90-8- State ofthe Clima te.
PMD. 2018. “Drought bulletin of Pakistan April–June 2018.” Islama-
bad. http:// www . ndmc. pmd. gov. pk/ quate r218. pdf. Accessed 1 Jan
Quiggin, John. 2010. “Drought, climate change and food prices in Aus-
tralia,” 11.www. uq. edu. au/ econo mics/ johnq uiggin. Accessed1
Nov 2019
Running, Steven W, Qiaozhen Mu, Maosheng Zhao, and Alvaro
Moreno. 2019. “MODIS global terrestrial evapotranspiration (ET)
product (MOD16A2/A3 and year-end gap-filled MOD16A2GF/
A3GF) NASA Earth observing system MODIS land algorithm.”
NASA EOSDIS land processes DAAC, 1–37. 10.5067/MODIS/
Shein KA (2006) State of the climate in 2005: executive summary.
Bull Am Meteor Soc 87(6):801–805. https:// doi. org/ 10. 1175/
BAMS- 87-6- 801
Thuiller W, Lavorel S, Araújo MB, Sykes MT, Colin I, Prentice. (2005)
Climate change threats to plant diversity in Europe. Proc Natl
Acad Sci USA 102(23):8245–8250. https:// doi. org/ 10. 1073/ pnas.
04099 02102
Vicca S, Balzarolo M, Filella I etal (2016) Remotely-sensed detection
of effects of extreme droughts on gross primaryproduction. Sci
Rep 6:28269. https:// doi. org/ 10. 1038/ srep2 8269
Vicente-Serrano SM (2007) Evaluating the impact of drought using
remote sensing in a Mediterranean, semi-arid region. Nat Hazards
40(1):173–208. https:// doi. org/ 10. 1007/ s11069- 006- 0009-7
Wang K, Li T, Wei J (2019) Exploring drought conditions in the three
river headwaters region from 2002 to 2011 using multiple drought
indices. Water (switzerland) 11(2):190. https:// doi. org/ 10. 3390/
w1102 0190
WMO, and GWP. 2016. Handbook of drought indicators and indices
(M. Svoboda and B.A. Fuchs). Integrated drought management
programme (IDMP). Integrated drought management programme
(IDMP), integrated drought management tools and guidelines
series 2. Geneva. Integrated drought management tools and
guidelines series. https:// doi. org/ 10. 1201/ 97813 15265 551- 12
Xu Y, Wang L, Ross KW, Liu C, Berry K (2018) Standardized soil
moisture index for drought monitoring based on soil moisture
active passive observations and 36 years of North American land
data assimilation system data: a case study in the southeast United
States. Remote Sensing 10(2):301. https:// doi. org/ 10. 3390/ rs100
Zargar A, Sadiq R, Naser B, Khan FI (2011) A review of drought indi-
ces. Environ Rev. https:// doi. org/ 10. 1139/ a11- 013
Zhang J, Qiaozhen Mu, Huang J (2016) Assessing the remotely sensed
drought severity index for agricultural drought monitoring and
impact analysis in north China. Ecol Ind 63(April):296–309.
https:// doi. org/ 10. 1016/j. ecoli nd. 2015. 11. 062
Publisher’s note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
427Global drought monitoring with drought severity index (DSI) using Google Earth Engine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
... With the unprecedented volume of different remote sensing datasets, the shift from ground-based observations to satellite-based sensors provides near real-time measurements, global coverage, and consistent and improved spatial resolution data records for the monitoring of droughts from a wide array of perspectives, such as agricultural and meteorological droughts [31,32]. In addition, the launch of Google Earth Engine (GEE), a big data cloud-based processing platform, in 2010 enabled users to access vast satellite datasets, and makes it possible to process such data for drought characterization and assessment [33][34][35][36][37]. Apart from the repeated observation capacity of the land surface, remote sensing sensors can provide measurements in regions that are either inaccessible or lack in-situ monitoring facilities for drought assessment [30]. ...
Full-text available
Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region.
Full-text available
This study was conducted to investigate the spatiotemporal variability in precipitation concentration over Iran. For that purpose, daily precipitation data with a spatial resolution of 0.25° × 0.25° from the Asfazari database for the period from 01/01/1962 to 31/12/2019 were used. Three indices including the precipitation concentration index (PCI), precipitation concentration period (PCP), and precipitation concentration degree (PCD) were utilized for examination of the variability in precipitation concentration over the country. The results demonstrated that the central, south-eastern, and eastern parts of the country exhibited maximum temporal precipitation concentration, while the least precipitation concentration could be observed over the Caspian coasts and the northern half of the country. The year 1998 was selected as the change point due to the considerable difference in the values of the examined indices, and the long-term statistical period was divided into two sub-periods before and after the change. During the sub-period after the change point (1999-2019), precipitation concentration has increased in the western, central, eastern, and south-eastern parts of Iran, according to PCI and PCD, and has decreased in the North and Northeast and along the northern coastline of Oman Sea. Furthermore, there have been great spatial differences in the period of occurrence of precipitation along the Northern coasts, according to PCP, varying from November, along the Caspian coasts, to August, along the northern foothills of Alborz Mountains. The PCP index has increased during the sub-period after the change point along the northern coastlines of Persian Gulf and Oman Sea and in parts of the North (along Alborz Mountains), indicating a shift in the period of precipitation from winter to the warm seasons of spring and summer. Moreover, the decrease in PCP in the Northwest and Northeast suggested that the period of occurrence of precipitation has shifted from the second half of winter toward early winter and late fall. After the year of change point, the frequency of rainy days and precipitation have decreased, and PCI and PCD have increased.
Technical Report
Full-text available
The Global Climate Risk Index 2020 analyses to what extent countries and regions have been affected by impacts of weather-related loss events (storms, floods, heatwaves etc.). The most recent data available— for 2018 and from 1999 to 2018 —were taken into account. The countries and territories affected most in 2018 were Japan, the Philippines as well as Germany. For the period from 1999 to 2018 Puerto Rico, Myanmar and Haiti rank highest. This year’s 15th edition of the Climate Risk Index clearly shows: Signs of escalating climate change can no longer be ignored – on any continent or in any region. Impacts from extreme weather events hit the poorest countries hardest as these are particularly vulnerable to the damaging effects of a hazard and have a lower coping capacity and may need more time to rebuild and recover. The Climate Risk Index may serve as a red flag for already existing vulnerabilities that may further increase as extreme events will become more frequent or more severe due to climate change. The heatwaves in Europe, North America and Japan also confirm: High-income countries are feeling climate impacts more clearly than ever before. Effective climate change mitigation is therefore in the self-interest of all countries worldwide.
Full-text available
Droughts continue to affect ecosystems, communities, and entire economies. Agriculture bears much of the impact, and in many countries it is the most heavily affected sector. Over the past decades, efforts have been made to assess drought risk at different spatial scales. Here, we present for the first time an integrated assessment of drought risk for both irrigated and rain-fed agricultural systems at the global scale. Composite hazard indicators were calculated for irrigated and rain-fed systems separately using different drought indices based on historical climate conditions (1980–2016). Exposure was analyzed for irrigated and non-irrigated crops. Vulnerability was assessed through a social-ecological systems perspective, using social-ecological susceptibility and lack of coping capacity indicators that were weighted by drought experts from around the world. The analysis shows that drought risk of rain-fed and irrigated agricultural systems displays heterogeneous pattern at the global level with higher risk for southeastern Europe, as well as northern and southern Africa. By providing information on the drivers and spatial patterns of drought risk in all dimensions of hazard, exposure, and vulnerability, the presented analysis can support the identification of tailored measures to reduce drought risk and increase the resilience of agricultural systems.
Full-text available
The Three River Headwaters Region (TRHR) has great uncertainty on drought conditions under climate change. The aim of this study is to compare the drought conditions detected by multiple drought indices across the TRHR. We applied four single drought indices, i.e., Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI), and two combined drought indices, i.e., Combined Meteorological Drought Index (CMDI) and Combined Vegetation drought index (CVDI), to explore the drought conditions across the TRHR. Three in situ drought indices, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Non-Parametric Index (SNPI) were used to evaluate the performances of multiple drought indices. The results include various drought conditions detected by multiple drought indices, as well as a comparative study among different drought indices. Through the comparative study, we found that PCI was a desirable single index to monitor meteorological drought. TCI was suitable for monitoring agricultural/vegetation drought. SMCI and VCI should be avoided for monitoring drought in this region. CMDI was an appropriate meteorological drought index, and CVDI was a promising indicator in monitoring agricultural/vegetation drought.
Full-text available
In 2017, the dominant greenhouse gases released into Earth's atmosphere-carbon dioxide, methane, and nitrous oxide-reached new record highs. The annual global average carbon dioxide concentration at Earth's surface for 2017 was 405.0 ± 0.1 ppm, 2.2 ppm greater than for 2016 and the highest in the modern atmospheric measurement record and in ice core records dating back as far as 800 000 years. The global growth rate of CO2 has nearly quadrupled since the early 1960s. With ENSO-neutral conditions present in the central and eastern equatorial Pacific Ocean during most of the year and weak La Niña conditions notable at the start and end, the global temperature across land and ocean surfaces ranked as the second or third highest, depending on the dataset, since records began in the mid-to-late 1800s. Notably, it was the warmest non-El Niño year in the instrumental record. Above Earth's surface, the annual lower tropospheric temperature was also either second or third highest according to all datasets analyzed. The lower stratospheric temperature was about 0.2°C higher than the record cold temperature of 2016 according to most of the in situ and satellite datasets. Several countries, including Argentina, Uruguay, Spain, and Bulgaria, reported record high annual temperatures. Mexico broke its annual record for the fourth consecutive year. On 27 January, the temperature reached 43.4°C at Puerto Madryn, Argentina-the highest temperature recorded so far south (43°S) anywhere in the world. On 28 May in Turbat, western Pakistan, the high of 53.5°C tied Pakistan's all-time highest temperature and became the world-record highest temperature for May. In the Arctic, the 2017 land surface temperature was 1.6°C above the 1981-2010 average, the second highest since the record began in 1900, behind only 2016. The five highest annual Arctic temperatures have all occurred since 2007. Exceptionally high temperatures were observed in the permafrost across the Arctic, with record values reported in much of Alaska and northwestern Canada. In August, high sea surface temperature (SST) records were broken for the Chukchi Sea, with some regions as warm as +11°C, or 3° to 4°C warmer than the longterm mean (1982-present). According to paleoclimate studies, today's abnormally warm Arctic air and SSTs have not been observed in the last 2000 years. The increasing temperatures have led to decreasing Arctic sea ice extent and thickness. On 7 March, sea ice extent at the end of the growth season saw its lowest maximum in the 37-year satellite record, covering 8% less area than the 1981-2010 average. The Arctic sea ice minimum on 13 September was the eighth lowest on record and covered 25% less area than the long-term mean. Preliminary data indicate that glaciers across the world lost mass for the 38th consecutive year on record; the declines are remarkably consistent from region to region. Cumulatively since 1980, this loss is equivalent to slicing 22 meters off the top of the average glacier. Antarctic sea ice extent remained below average for all of 2017, with record lows during the first four months. Over the continent, the austral summer seasonal melt extent and melt index were the second highest since 2005, mostly due to strong positive anomalies of air temperature over most of the West Antarctic coast. In contrast, the East Antarctic Plateau saw record low mean temperatures in March. The year was also distinguished by the second smallest Antarctic ozone hole observed since 1988. Across the global oceans, the overall long-term SST warming trend remained strong. Although SST cooled slightly from 2016 to 2017, the last three years produced the three highest annual values observed; these high anomalies have been associated with widespread coral bleaching. The most recent global coral bleaching lasted three full years, June 2014 to May 2017, and was the longest, most widespread, and almost certainly most destructive such event on record. Global integrals of 0-700-m and 0-2000-m ocean heat content reached record highs in 2017, and global mean sea level during the year became the highest annual average in the 25-year satellite altimetry record, rising to 77 mm above the 1993 average. In the tropics, 2017 saw 85 named tropical storms, slightly above the 1981-2010 average of 82. The North Atlantic basin was the only basin that featured an above-normal season, its seventh most active in the 164-year record. Three hurricanes in the basin were especially notable. Harvey produced record rainfall totals in areas of Texas and Louisiana, including a storm total of 1538.7 mm near Beaumont, Texas, which far exceeds the previous known U.S. tropical cyclone record of 1320.8 mm. Irma was the strongest tropical cyclone globally in 2017 and the strongest Atlantic hurricane outside of the Gulf of Mexico and Caribbean on record with maximum winds of 295 km h⁻¹. Maria caused catastrophic destruction across the Caribbean Islands, including devastating wind damage and flooding across Puerto Rico. Elsewhere, the western North Pacific, South Indian, and Australian basins were all particularly quiet. Precipitation over global land areas in 2017 was clearly above the long-term average. Among noteworthy regional precipitation records in 2017, Russia reported its second wettest year on record (after 2013) and Norway experienced its sixth wettest year since records began in 1900. Across India, heavy rain and flood-related incidents during the monsoon season claimed around 800 lives. In August and September, above-normal precipitation triggered the most devastating floods in more than a decade in the Venezuelan states of Bolívar and Delta Amacuro. In Nigeria, heavy rain during August and September caused the Niger and Benue Rivers to overflow, bringing floods that displaced more than 100 000 people. Global fire activity was the lowest since at least 2003; however, high activity occurred in parts of North America, South America, and Europe, with an unusually long season in Spain and Portugal, which had their second and third driest years on record, respectively. Devastating fires impacted British Columbia, destroying 1.2 million hectares of timber, bush, and grassland, due in part to the region's driest summer on record. In the United States, an extreme western wildfire season burned over 4 million hectares; the total costs of $18 billion tripled the previous U.S. annual wildfire cost record set in 1991.
Full-text available
In Northeast Brazil (NEB), severe droughts have high socioeconomic impacts. In this study, the spatial–temporal characteristics of drought were evaluated based on a new drought index at 4-km spatial resolution, derived from regional empirical relationships between a remote sensing-based index and rain-gauge-based standardized precipitation index (SPI), a well-known drought meteorological index. This index was used to compare the spatial pattern of severe drought events (1982–1983, 1992–1993, 1997–1998, and 2012–2013) of the last 30 years. Strong El Niño related droughts were found to be generally spatially limited, affecting around 30% of NEB and concentrated in the northern part of the region, while 2012 drought, which was not El Niño related, was widespread, reaching 46% of NEB. These results stressed the importance of analyzing droughts at the subregion scale using data with higher spatial resolution. Statistically significant trends (p< 0.05) toward drier conditions detected in the SPI time-series were linked to the tropical Atlantic Ocean warming trend, which result in an increased drought risk and social vulnerability in the region.
Full-text available
Currently, the high-resolution satellite images in near real-time have gained more popularity for natural disaster detection due to the unavailability and difficulty of acquiring frequent ground observation data over a wide region. In Sudan, the occurrence of drought events is a predominant natural disaster that causes substantial damages to crop production. Therefore, monitoring drought and measuring its impact on the agricultural sector remain major concerns of policymakers. The current study focused on assessing and analyzing drought characteristics based on two meteorological drought indices, namely the Standardized Precipitation Index (SPI) and the Drought Severity Index (DSI), and inferred the impact of drought on sorghum productivity in Sudan from 2001 to 2011. To identify the wet and dry areas, the deviations of tropical rainfall measuring mission (TRMM) precipitation products from the long-term mean from 2001 to 2011 were computed and mapped at a seasonal scale (July–October). Our findings indicated that the dry condition fluctuated over the whole of Sudan at various temporal and spatial scales. The DSI results showed that both the Kordofan and Darfur regions were affected by drought in the period 2001–2005, whereas most regions were affected by drought from 2008 to 2011. The spatial correlation between DSI, SPI-3, and TRMM precipitation products illustrated a significant positive correlation in agricultural lands and negative correlation in mountainous areas. The relationship between DSI and the Standardized variable of crop yield (St. Y) for sorghum yield was also investigated over two main agricultural regions (Central and Eastern regions) for the period 2001–2011, which revealed a good agreement between them, and a huge drop of sorghum yield also occurred in 2008–2011, corresponding to extreme drought indicated by DSI. The present study indicated that DSI can be used for agricultural drought monitoring and served as an alternative indicator for the estimation of crop yield over Sudan in some levels.
Full-text available
The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system. This study compares six drought indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Soil Moisture Deficit Index (SMDI), Aggregate Drought Index (ADI), and Standardized Runoff-discharge Index (SRI), and evaluates their performance with respect to identifying historic drought events in the UBN basin. The indices were calculated using monthly time series of observed precipitation, average temperature, river discharge, and modeled evapotranspiration and soil moisture from 1970 to 2010. The Pearson’s correlation coefficients between the six drought indices were analyzed. SPI and SPEI at 3-month aggregate period showed high correlation with ETDI and SMDI (r > 0.62), while SPI and SPEI at 12-month aggregate period correlate better with SRI. The performance of the six drought indices in identifying historic droughts: 1973–1974, 1983–1984, 1994–1995, and 2003–2004 was analyzed using data obtained from Emergency Events Database (EM-DAT) and previous studies. When drought onset dates indicated by the six drought indices are compared with that in the EM-DAT. SPI, and SPEI showed early onsets of drought events, except 2003–2004 drought for which the onset date was unavailable in EM-DAT. Similarly, ETDI, SMDI and SRI-3 showed early onset for two drought events and late onsets in one-drought event. In contrast, ADI showed late onsets for two drought events and early onset for one drought event. None of the six drought indices could individually identify the onsets of all the selected historic drought events; however, they may identify the onsets when combined by considering several input variables at different aggregate periods.
Full-text available
Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index—standardized soil moisture index (SSI)—by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the North American Land Data Assimilation System (NLDAS) Noah land surface model (LSM) output. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer drought severity index (PDSI) and normalized difference water index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.
Full-text available
Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.