This document discusses various definitions and approaches for monitoring and assessing agricultural drought. It begins by outlining definitions of drought from various organizations like FAO, WMO, and IMD. It then discusses different indices used to monitor drought, including the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Vegetation Drought Response Index (VegDRI), and Process-based Accumulated Drought Index (PADI). The PADI is proposed as a new index under the Evolution Process-based Multi-sensor Collaboration (EPMC) framework to assess drought impacts on regional crops. The document compares the PADI to SPI at different timescales and crop yield loss data in three climatic

1. ABHILASH
Ph.D. Student
Dept. of Agricultural Meteorology
CCS Haryana Agricultural University, Hisar

2. DROUGHT DEFINITION
 No universal definition
 FAO (1983) defines drought hazard as “the percentage of years
when crop fails from the lack of moisture.”
 WMO (1986) “drought means a sustained, extended deficiency
in precipitation”
 IMD (1967) “Drought is the consequence of a natural reduction
in the amount of precipitation over an extended period of time,
usually a season or more in length, often associated with other
climatic factors (viz. high temperatures, high winds and low
relative humidity) that can aggravate the severity of the
drought event.”
 IPCC AR5 (2014) defines drought hazard as “a period of
abnormally dry weather long enough to cause a serious
hydrological imbalance”.

3. Source: National Drought Mitigation Center, University of Nebraska – Lincoln, U.S.A
Natural Climatic Variability
Precipitation deficiency High temperature, high
winds, low R.H., greater
sunshine, less cloud cover
Soil water deficiency
Plant water stress,
reduced biomass and yield
Reduced stream flow,
inflow to reservoirs, lakes
and ponds
HydrologicalAgriculturalMeteorological
DroughtDroughtDrought
Socio-economic impact
Time(duration)

4. Importance of Monitoring and Forecasting of
Agricultural Drought
 The frequency of drought in India is increasing.
1950-1990 – 10 drought years
since 2000 – 6 drought years (2002, 2004, 2009, 2014 ,2015 and 2016)
 68 % of the net sown area in India is prone to drought.
 Monitoring and forecasting are part of preparedness that helps in
reducing the impact of drought by following practices:
 Selection of crop management practices
 Contingency planning
 Policy formulation
 Crop/ Livestock insurance
 Watershed management
 Agro-advisories
 Monitoring helps in providing relief measures to severely affected
areas.

5. AGRICULTURAL DROUGHT INDICES
Indices Author Description Advantages Limitations
Standardized
Precipitation
Index (SPI)
McKee et
al. (1992) at
Colorado
State
University,
USA
Uses only
precipitation
data
Simple and
measures
drought
conditions at
different time
scales
Use only
precipitation, hard
to
interpolate over
large areas
Standardized
Precipitation
Evapotranspiratio
n Index (SPEI)
Vicente-
Serrano
(2010) at
Pyrenean
Institute of
Ecology,
Spain
Based on SPI
requiring
precipitation and
PET data
Simple, adaptable
to different time
scales and
regions, spatial
comparison
possible
Accuracy depends
upon length of
precipitation and
temperature
record
Stress Degree
Days (SDD)
Jackson
et al.,
(1977)
Uses Canopy and
air temperature
A simple measure
calculated by the
difference
between canopy
and air
temperature
Environmental
conditions such as
air
humidity and soil
moisture can
affect
the index

6. AGRICULTURAL DROUGHT INDICES
Indices Author Description Advantages Limitations
Palmer Drought
Severity Index
(PDSI)
Palmer
(1965)
Uses precipitation
and temperature
to estimate
moisture supply
and demand in soil
Calculate soil
moisture content
to estimate
drought
Slow respone; less
suited for higher
elevation and
winter period
having snow
Crop Moisture
Index (CMI)
Palmer
(1968)
Uses Weekly
precipitation,
weekly mean
temperature and
previous week’s
CMI value.
Identifies
potential
agricultural
droughts
Not useful in long
term drought
monitoring
Crop Specific
Drought Index
(CSDI)
Meyer
et al.
(early
1990s) at
the
University
of
Nebraska-
Lincoln
Uses temperature,
precipitation and
evapotranspiration
data
Provides daily
estimates of soil
water
availability for
different zones
and
soil layers
Too many
requirements
including soil
type, crop
phenology, and
climatological
data

7. AGRICULTURAL DROUGHT INDICES
Indices Author Description Advantages Limitations
Aridity Anomaly
Index (AAI)
Developed in
India by IMD
Based on
Thornthwaite’s
(1948) water
balance
technique
AET and PET values
are used
Simple calculations.
Responds quickly
with a weekly time
step.
Not applicable
to long-term
or
multiseasonal
events
Soil Moisture
Deficit Index
(SMDI)
Narasimhan
and Srinivasan
(2004) at the
Texas
Agricultural
Experiment
Station, United
States
Weekly soil
moisture and
ET values
simulated by the
Soil and Water
Assessment Tool
(SWAT)
Improves the ability
for modeling
and monitoring
hydrological system &
soil moisture deficit
at a finer
Resolution
Irrespective to
soil properties
across
different
climatic
conditions
Normalized
Difference
Vegetation Index
(NDVI)
Developed by
Tarpley et al.
and Kogan
while working
with NOAA,
United States
(1984)
(NIR-R)/(NIR+R)
Simple Calculations.
Measure crop
vigour
Slow to
respond

8. Solar Induced Chlorophyll Fluorescence (SIF)
 Extracted by Joiner et al. (2014) using GOME – 2 spectrometer onboard
MetOp – A satellite at 740 nm using radiative transfer model.
 First study for drought monitoring and assessment – Wang et al.
(2016) in the great plains of U.S.A. mainly South Dakota (39),
Nebraska (25) and Kansas (14)

9. Correlation coefficients of short term SPIs and SIF, NDVI and NDWI from June to August,
2012. Results of Climatic Divisions 1401, 1402, 2501 and 3901 are indicated by (a – d),
respectively. ‘n’ is sample number for regression analysis and ‘p’ is lowest significance
level

10. For SPI-3 (Lower values are
deeper red which indicate less
precipitation)
%SIF reduction in
2012
%NDVI reduction in
2012
Lack of precipitation over the Great Plains from May to October in 2012

11. The Vegetation Drought Response Index (VegDRI): A New
Integrated Approach for Monitoring Drought Stress in
Vegetation
(Brown et al. ,2008)
 VegDRI integrates historical climate data and satellite – based earth
observations with other biophysical parameters to produce 1 km resolution
indicator of the geographical extent and intensity of drought stress on
vegetation
 Study conducted over seven state regions of United States for 2002
7 states with
geographic locations of
776 weather stations
used for VegDRI
development

12. Data inputs for the VegDRI model
(17 years database i.e. 1989 to 2005)
Data set name Type Source Time step
Standardized Precipitation
Index (SPI)
Climate ACIS/NADSS biweekly
Palmer Drought Severity
Index (Self – calibrated)
(PDSI)
Climate ACIS/NADSS biweekly
Percent of Average
Seasonal Greenness (PASG)
Satellite AVHRR NDVI
14 day NDVI
composites
Start of Season Anomaly
(SOSA)
Satellite AVHRR NDVI
14 day NDVI
composites
Land Cover (NLCD) Biophysical
National Land
Cover Database
constant
Soil Available Water
Capacity (AWC)
Biophysical
STATSGO
constant
Irrigated Agriculture
(IrrAg)
Biophysical
USDA NASS
constant
Ecological Regions (ECO) Biophysical EPA Ecoregions
constant

13. Calculation of Percent of Average Seasonal Greenness (PASG):
SG – Seasonal Greenness for each bi-weekly period of the growing season
for all 17 years in the time series. Represents accumulated NDVI above a
baseline (NDVIb) across 14 days within a bi-weekly period.
SOS and EOS are median start and end day of the growing period,
respectively
P1, P2, P3...., Pn refers to –> 14 day periods
SGPnYn - SG for a bi-weekly period (Pn) of a specific year (Yn)
xSGPn - historical average SG(x) for the same biweekly period (Pn)

14. Calculation of Start of Season Anomaly (SOSA):
Departure in the SOS for a specific year from the average
historical SOS for a given pixel.
SOSAi - SOSA (in number of days) for year i
SOSi - start of season DOY for year i
SOSmed – median start of season DOY from 1989 to 2005
The SOSA was included in the VegDRI model to account for the different
timings of emergence of various natural and agricultural vegetation types,
as well as land cover change — all of which can influence seasonal
vegetation performance

15. Steps for VegDRI development:
Step 1: process, summarize, and organize the data for the 8
variables.
Information subdivided into three seasonal phases for model
development.

16. Steps for VegDRI development:
Step 2: generation of empirically derived model for each phase
by applying a supervised Classification and Regression Tree
(CART) analysis technique to information in the database by
Cubist algorithm.
It generate three seasonal, rule-based, piecewise linear
regression VegDRI models.
Example:
Rule 1:
If: 52 week SPI ≤ 1.4
Land Cover in {Grassland, Pasture/Hay, Row Crops}
AWC ≤ 5.46
Percent Irrigation ≤ 6
then:
VegDRI = -4.9+1.48 (52 week SPI) + 3.2(Percent Irrigation)-0.14AWC
31, 26 and 29 rules for spring, summer and fall phases, respectively

17. Steps for VegDRI development:
Step 3: rules from the appropriate seasonal Cubist model were
applied to gridded image input data for each bi-weekly period
using MapCubist software to produce series of 1 km VegDRI
maps across growing season.
SPI and PDSI– point based – 1 km raster image generated for
each bi-weekly period using Inverse Distance Weighting
interpolation method.
Cross-Validation: Cross validation by using 16 years data for
training and one year for testing. Iterative process.

18. Results
VegDRI map for March
10, 2015
USDM map for March
12, 2015

19. Integrated Surface Drought Index (ISDI)
Zhou et al. (2013) in China
Based on VegDRI, but additional Vegetation Supply Water Index
included.
VSWI= NDVIij / LSTij
NDVIij - 16 day NDVI in period i for year j
LSTij - Land Surface Temperature in period I for year j
Rule:
If: -11< SOSA ≤ -7
66.8< Elevation ≤ 125.5
AWC ≤ 235
Land Cover in {Woody Savannas, Grasslands, or
Cropland/Natural Vegetation Mosaic}
then: ISDI = 1.1453 + 0.74SOSA + 0.81SPI + 1.19VWSI

20. Further improvement:  increasing spatial resolution
 new inputs like Enhanced Vegetation
Index (EVI) or LAI to avoid NDVI
saturation
Drought degree classification of ISDI
Grades Class ISDI
1 Normal -1 to +1
2 Mild -2 to -1
3 Moderate -3 to -2
4 Severe -4 to -3
5 Extreme <-4
Comparison between accuracy of VegDRI and ISDI

21. OBJECTIVES
a) To design a collaborative monitoring framework based on multi-sensor
approach for the drought evolution process, referred to as the
Evolution Process-based Multi-sensor Collaboration (EPMC) framework.
b) To propose a Process-based Accumulated Drought Index (PADI) under
the EPMC framework to assess drought impacts on regional crops.
c) To compare PADI with multi-time scale SPI, and crop yield loss data
in three different climatic regions.

22. STUDY AREA
(A) Subtropical humid monsoon region of Hubei province; (B) tropical humid monsoon
region of Yunnan province, and (C) semi-arid temperature region of Hebei province

23. DATA and METHODOLOGY
 30 years precipitation data – Global Precipitation Climatology Center
(GPCC)
 SPI calculated at 3-, 6-, 9-, and 12-month time scales using the
Standardized Drought analysis Toolbox
 Penman – Monteith (PM) model based PDSI data – National Center for
Atmospheric Research (NCAR)
 Root zone soil moisture data – Global Land Data Assimilation System
version 2 (GLDAS-2.0) Noah Land Surface Model L4 product (monthly) with
0.25° spacing from 1980 for 30 years to calculate
Soil Moisture Condition Index (SMCI)
where
SM, SMmax, and SMmin
are the pixel values of root zone soil moisture, its maximum
and minimum, respectively, during the same month in the past 30 years.

24. DATA and METHODOLOGY
 Precipitation data was obtained from Global Precipitation Climatology
Centre (GPCC) to calculate
Precipitation Condition Index (PCI)
where
P, Pmax and Pmin are pixel values of precipitation, its maximum and
minimum value, respectively
 30 years NDVI data was collected using – Advanced Very High Resolution
Radiometer (AVHRR) Vegetation Health Product (VHP) from National
oceanic and Atmospheric Administrations (NOOA) centre for Satellite
Application and Research to calculate
Vegetation Condition Index (VCI)
where
NDVI, NDVImax, and NDVImin are the pixel values of NDVI.
Maximum & minimum are considered during same week in past 30 years.

25. Flowchart of EPMC and basic evolution process of agricultural
drought

26. Process – based Accumulated Drought Index (PADI)

27. Process – based Accumulated Drought Index (PADI)
PADIt – index value at time t
T – duration of assessment period (7 days)
Si – different crop growth stages in the study area
n – total number of growth stages
p2 and p3 – onset and development phase, respectively
- duration of the intersection between the current
calculation week, growth stage I, and the onset phase
λi – water-deficit sensitivity coefficient in the growth stage i

28. Agricultural Drought Classification based on PADI
PADI value Classification
0 - 0.20 Mild drought
0.21 – 0.40 Moderate drought
0.41 – 0.60 Severe drought
0.61 – 0.80 Extreme drought
0.81 – 1.00 Exceptional drought
Four main growth stages of wheat in regions A,B and C along with
their water sensitivity coefficient

29. Mean and standard deviations of PCI, SMCI, and VCI under EPMC framework
Region A
Oct Dec Jan Mar Apr May Jun

30. Mean and standard deviations of PCI, SMCI, and VCI under EPMC framework
Region B
Region C

31. Agricultural drought severity mapping using PADI for region A from
Jan, 2011 to May, 2011

32. Agricultural drought severity mapping using PADI for region B from
Feb, 2010 to May, 2010

33. Agricultural drought severity mapping using PADI for region C from
Sept, 2009 to June, 2010

34. Correlation analysis of PADI with SPI-3, SPI-6, SPI-9, and SPI-12
from Jan, 2011 to May, 2011 in region A

35. Correlation analysis of PADI with SPI-3, SPI-6, SPI-9, and SPI-12
from Feb, 2010 to May, 2010 in region B

36. Correlation analysis of PADI with SPI-3, SPI-6, SPI-9, and SPI-12 from
Sep, 2009 to June, 2010 in region C

37. Scatter plot and correlation analysis for region A in 2011

38. Scatter plot and correlation analysis for region B in 2010

39. Scatter plot and correlation analysis for region C in 2010

40. Predicting agricultural drought in eastern Rajasthan of
India using NDVI and standardized precipitation index
Dutta et al. (2013)
Study area
STUDY AREA 

41. METHODOLOGY
 Rainfall data : Daily data from 1984 to 2003 was used for
SPI computation
 Crop yield statistics : three major kharif crops i.e. sorghum,
pearl millet and maize of each 21 districts of Eastern
Rajasthan from http://www.rajasthankrishi.gov.in
 Satellite data : NDVI derived from AVHRR onboard NOAA. 15
day composite with spatial resolution of 8 km.
 Multiple Regression Model with two variables, SPI and
previous NDVI, used to predict NDVI of coming fortnight
 Regression model was developed for predicting dominant
crop yield from NDVI

42. RESULTS
Correlation coefficients at different agro-climatic zones

43. NDVI prediction models
Multiple Regression Models for NDVI prediction in the transitional
zone
Note: SPI-1 = SPI of previous fortnight, NDVI-1 = NDVI of previous fortnight,
SPI 3 = cumulative rainfall of previous three fortnights, SPI 6 = cumulative rainfall of
previous 6 fortnights and SEE = standard error of estimate

44. NDVI prediction models
Multiple Regression Models for NDVI prediction in the semi-arid zone
Note: SPI-1 = SPI of previous fortnight, NDVI-1 = NDVI of previous fortnight,
SPI 3 = cumulative rainfall of previous three fortnights, SPI 6 = cumulative rainfall of
previous 6 fortnights and SEE = standard error of estimate

45. Predicted and observed NDVI in (A) drought year 2002, and
(B) non-drought year 2003
Transitional Zone
Semi-arid Zone

46. Correlation coefficients of fortnightly NDVI and sorghum yield
Crop yield prediction models

47. Observed and predicted yield of dominant crop

48. OBJECTIVES
a) Develop a drought forecast model based on the combination of
remote sensing and long-range forecast data using machine
learning for ungauged areas
b) Provide improved ranges of drought forecast in case of the
improvement of forecasting skill of the long-range forecast data

49. Study area : South Korea
Data:
 Monthly precipitation and temperature data from 61 Automatic Synoptic
Observation System (ASOS)
 Potential Evapotranspiration – mean air temperature using Thornthwaite
method
 SPI and Standardized Precipitation Evapotranspiration Index (SPEI) with
3-, 6-, 9-, and 12- month time scale were used with lead times from 1 to 6
months.

50. Remote Sensing data used for ungauged stations

51. Classification Index Value
Extremely Wet (EW) ≥2.00
Very Wet (VW) 1.50 - 1.99
Moderately Wet (MW) 1.00 – 1.49
Near Normal (NN) From 0.99 to – 0.99
Moderate Draught (MD) From -1.00 to -1.49
Severe Drought (SD) From -1.50 to -1.99
Extreme Drought (ED) ≤ -2.00
Drought condition classifications for SPI and SPEI (McKee et al., 1993).
Six Global Climate Models (GCMs) used in this study to obtain long range
climate forecast data

52. Drought category classification accuracy measures

53. Drought forecasting at gauge locations
 For each station locations, SPI and SPEI were calculated.
 For each lead time, observation data were used for the past period,
and two methods were used to fill the future period:
1. Climatology data were obtained from the median value of 100
samples randomly derived from the observation data of the same
month (C – method)
2. Long-range forecast data : percent increment of modeled
precipitation or temperature anomaly is applied to observational
climatology data to form the calibrated model data (F – method)
Drought forecasting for ungauged areas
 Following machine learning models (ML – method)
1. Decision Trees (DT)
2. Random forest (RF)
3. Extremely randomized trees (ERT)
 Kriging (gaussian process) as spatial interpolation baseline method
(I – method)

54. Structure of each machine learning model

55. RESULTS
Producer’s Drought Accuracy of SPI and SPEI forecasts with 3-, 6-, 9-, and 12-month
time scales and lead times of 1–6 months based on C-method and F-method.

56. Producer’s Drought Accuracy of SPI and SPEI forecasts with 3-, 6-, 9-, and 12-month time
scales and lead times of 1–6 months based on C-I, F-I, C-ML (DT, RF, ERT model), and F-ML
(DT, RF, ERT model) methods

57. Producer’s Drought Accuracy of SPI and SPEI forecasts with 3-, 6-, 9-, and 12-month time
scales and lead times of 1–6 months based on C-I, F-I, C-ML (DT, RF, ERT model), and F-ML
(DT, RF, ERT model) methods

58. CONCLUSION
 Drought monitoring accuracy and detail can be improved by
integrating various climate (precipitation and temperature),
vegetation (phenological stage of crop growth) and biophysical (soil
moisture holding capacity, land use change, irrigation and ecological
regions) parameters to produce one drought index through
classification and regression tree approach.
 Considering the cumulative effect of evolution of drought stages on
the respective crop growth stage can further improve the process of
drought monitoring.
 Both fast response factor like SIF and slow response factor like NDVI
is necessary for drought monitoring and forecasting.
 Drought forecasting can be done either by multiple linear regression
model which is region specific and empirical or by new approaches
applicable to every region like Machine Learning which should be
based on large-scale climate indices and long range local
climatological data.