1) Drought is a recurrent lack of precipitation that affects different regions in varying ways. It can be defined meteorologically, agriculturally, hydrologically, or socioeconomically. 2) Key indicators for monitoring drought include rainfall, snowpack, soil moisture, temperature, streamflow, groundwater, reservoir and lake levels, and evapotranspiration. Triggers are specific indicator values that initiate and terminate drought response levels. 3) Remote sensing data from satellites can be used to monitor drought indicators like vegetation health and soil moisture over large areas.

1. Strengthening Water Resources Management in
Afghanistan (SWaRMA)
Training Workshop on Multi-scale Integrated River Basin Management from a HKH perspective
Drought monitoring and Early warning system

2. Drought is a normal, recurrent feature of climate. It occurs
almost everywhere, although its features vary from region
to region. Defining drought is therefore difficult
Operational definitions of drought
Meteorological drought (deficit in a specific region)
Agricultural drought (not enough soil moisture for crop needs)
Hydrological drought (deficiency in water suppliers at river
basin scale)
Socioeconomic drought (affect people and food supply)
Conceptual definition of drought
A protracted period of deficient precipitation
resulting in extensive damage to crops, resulting in
loss of yield
What is drought?

3. Key indicators for monitoring drought and establishing trigger
• rainfall
• snow pack
• soil moisture
• temperature
• stream flow
• ground water
• reservoir and lake levels
• Evapotranspiration/ effective precipitation
• vegetation health/stress and fire danger
drought trigger in irrigated system
Triggers: Specific values of the indicator that
initiate and terminate each level of a drought
plan, and associated management responses.
Indicators: Variables to describe drought conditions.
drought trigger in rainfed system
If predicted or
actual rainfall is
below

4. Remote Sensing for monitoring drought indicators

5. Remote sensing data product validation
Evaluation of Gridded Rainfall Data Products for Drought
Monitoring in South Asia.
observed station data and Koppen climate classification
• Station precipitation data (140) 1981-2012
• Aphrodite (stations interpolated gridded
data)
• CHIRP and CHIRPS

6. Drought indices
Indices are typically computed numerical representations of drought severity, assessed using climatic or
hydrometeorological inputs including the drought indicators. They aim to measure the qualitative state of
droughts on the landscape for a given time period.
• Simplify complex relationships and provide
a good communication tool for diverse
audiences
• Quantitative assessment of anomalous
climatic conditions
• Intensity
• Duration
• Spatial extent
• Historical reference (probability of
recurrence)
• Planning and design applications
Source: Svoboda, 2009

7. Percent of Normal:
• simple measurement
• appeals to the public as easy to understand
• calculated by dividing actual precipitation by
normal precipitation (generally a 30-year mean)
and multiplying x 100%
• easily misunderstood…as the mean and the
median are often not the same
Drought indices Decile:
• Developed in 1967 (Gibbs and Maher)
• Relatively easy to calculate
• grouped into 5 classifications (see table)
• distribution of occurrences divided into tenths
• need a long period of record to be accurate
(most simple examples)
Where:
Di is the ith decile
k is the decile and
N = number of observations

8. Drought indices…
8
Input based indices Output based indices
Rainfall based indices –
 Percent deviation from normal
 Standardized precipitation index (SPI)
Soil moisture based indices –
 Actual crop ET / Potential crop ET
 Crop yield loss based indices
 Remote sensing based indices
Rainfall
Actual evapotranspiration/ Vegetation indices
Soil moisture
Input Output
Rainfall
Ground Water

9. Advantages and disadvantages….
9
Input (rainfall) based indices
(Rainfall deviations, SPI…)
Output based indices
(WRSI, NDVI, …)
Advantages
 Quick to compute and simple, low-cost,
objective, transparent, and reliable
 Closer to reality - based on the actual
responses of crop, which in turn are
driven by the actual soil water
availability in the crop root zone
Disadvantages
o Crop independent
o Implied relationship of crop responses to
actual soil water availability in the crop
root zone
o More detailed computational procedures
– more comprehensive inputs needed
and more assumptions involved
o Impact will be understood better when
nearing the end of season

10. • Simple index--precipitation is the only parameter
(probability of observed precipitation transformed into an
index)
• Multiple time scales allow for temporal flexibility in
evaluation of precipitation conditions and water supply
• Need 30 years of continuous monthly precipitation data
• It is NOT simply the “difference of precipitation from the
mean… divided by the standard deviation”
• Precipitation is normalized using a probability distribution
so that values of SPI are actually seen as standard
deviations from the median
• Normal distribution allows for estimating both dry and
wet periods
• Accumulated values can be used to analyze drought
severity
Standardized Precipitation Index (SPI):

11. Rice
Barley
Wheat
Maize
Selected major cereal crops and respective three critical months of rainfall
for crop growth
• Compilation of major crop area mask based on district agriculture census data
• Compilation of crop production anomaly statistics
• Computation of time series area under drought
• Computation of regression between area weighted drought values and crop
production
Use of SPI for retrospective analysis of agriculture
drought conditions in Nepal
Districts with High Crop Sown Area

12. Regression analysis between crop production anomaly and
area weighted drought conditions
Paddy (Jun-Jul-Aug)
Maize (Feb-Mar-Apr)
Wheat (Nov-Dec-Jan)
Barley (Oct-Nov-Dec)
(1981-2014)
SPI <= -1.0
0
1
2
3
4
5
6
7
8
9
10
11
Drought Frequency
Use of SPI for retrospective analysis of agriculture
drought conditions in Nepal

13. NDVI as indicator of Drought
NDVI = (NIR – Red) / (NIR + Red)
Low
High

14. 2018
Mean
2016

16. Temporal filtering of LST data
Original LST
Processed LST
Measuring heat stress to compliment drought indicators
TCI=(BTmax-BTmin)/(BTmax-BTmin)
Heat wave of June 2010

17. Vegetation condition index (VCI), values 0 - 100
VCI=(NDVI-NDVImin)/(NDVImax-NDVImin)
NDVImax, and NDVImin – climatology (2001-2015)
maximum and minimum NDVI for a pixel;
Temperature condition index (TCI), values 0 - 100
TCI=(BTmax-BTmin)/(BTmax-BTmin)
NDVImax, and NDVImin – climatology (2001-2015)
maximum and minimum NDVI for a pixel
Vegetation Health Index (VHI),values 0 – 100
VHI=a*VCI+(1-a)*TCI
0 – indicates extreme stress
100 – indicates favorable conditions
MOISTURE
THERMAL
VEG.
HEALTH
NDVI as indicator of Drought

18. TRMM
Rainfall
Normalized Difference Vegetation Index
Use of Remote Sensing for characterizing agriculture
drought in Koshi basin
Integration of climate data for improved understanding of drought
Where Ts stands for the temperature of
the crown layer of crop. The smaller
VSWI is, the severer the drought is.
VSWI = NDVI / TS
Vegetation supply water index (VSWI)

19. January March May
July September November
Normalized Vegetation Water
Supply Index (NVSWI)
0
50
100
150
200
250
300
350
400
0
10
20
30
40
50
60
70
80
Jan-07
Mar-07
May-07
Jul-07
Sep-07
Nov-07
Jan-08
Mar-08
May-08
Jul-08
Sep-08
Nov-08
Jan-09
Mar-09
May-09
Jul-09
Sep-09
Nov-09
Jan-10
Mar-10
May-10
Jul-10
Sep-10
Nov-10
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Nov-11
Mean Rainfall
Arid Agr
Irri. Agr
Rainfall(mm)
NVSWI

20. Integration of climate data for improved
understanding and early warning system
Moisture conditions and rainfall lag
time relation
• Integration of satellite based vegetation
index (NDVI), land surface temperature
(LST) and rainfall (TRMM) data at an
interval of every 16 days provide effective
operational means to monitor drought
conditions over large areas.
• Monitoring of preseason rainfall is
significantly important to track the
development of drought conditions.
0
0.2
0.4
0.6
0.8
1
0
16
32
48
64
80
96
112
128
Correlationcoefficient
Days ( lag time)
Arid. Agri Irri. Agri

21. http://tethys.icimod.org/apps/dmlocal/?c=Nepal&d=Jumla
&p=mm&i=rain,evap,soilMoist,tempExtreme
South Asian Land Data Assimilation for Drought Monitoring System in
Afghanistan, Bangladesh, Nepal and Pakistan
http://tethys.icimod.org/apps/bldas-explorer/

22. RIVER BASINS & CRYOSPHERE 22
Thank you