1. Anjali Singh, SRF, Indian Agricultural Research Institute (IARI)
Drought Hazard and Vulnerability Analysis for Bundelkhand Region using Geo-Spatial Tools
Anjali Singh, SRF,
Indian Agricultural Research Institute (IARI)
Supervisors (s) :
Dr. Anil K. Gupta, Ms Sreeja S. Nair, (NIDM), Dr. V. K. Sehgal, (IARI)
Dr. P. K. Joshi, (TERI University)

2. Objectives
1) To assess meteorological, hydrological and agricultural drought using suitable indices.
2) To identify districts exposed to extreme hazard and highly vulnerable to drought.
3) To prepare composite drought risk map for Bundelkhand region using geo-spatial tools.

3. Study Area
Comprises of 13 districts covering 70,000 sq. km distributed over U.P. and M.P.
It comes among the most backward region of India
Average rainfall with a range of 768 to 1087 mm
Net sown area is 3706’ 000 ha
This region faced consecutive drought since to
Legend
M.P.
U.P.

4. Materials and Methods Data acquisition
Meteorological- Monthly precipitation data from 1998 to 2009 (Indian Meteorological Department).
Hydrological- Monthly data of groundwater from 1998 to 2010 for 264 stations (Central Ground Water Board)
Agricultural- Satellite imageries from 1998 to 2009 downloaded from (
Satellite and Sensor
Type
Instrument
Format
Resolution
Region of Interest
Time Period
SPOT VEGETATION
S10
VGT 1
NDVI
1 km
SE-Asia
September
(1, 11, 21)
( )

5. Materials and Methods(2)
Softwares used
Software
Utility
ENVI 4.4
For image processing, district mask generation, agricultural mask application, NDVI and VCI computation
ArcGIS 9.1
For districts vector and raster file preparation, interpolation (surface layer creation) and maps preparation
Microsoft Excel (2007)
For data arrangement and using other calculations

6. Meteorological drought
Methodology
-Subsetting
- Agricultural
mask application
Phase I
Meteorological Data
Satellite Data
Hydrological Data
Pre-processed data
Literature Review
Selection of suitable indices
Meteorological drought
Agricultural drought
Hydrological drought

7. Composite Drought Risk Map
Methodology(2)
Phase II
Data Analysis
Frequency
Intensity
Chronology of drought
Phase III
Composite Drought Risk Map
Vulnerability Maps
Agricultural
Hydrological
Hazard Map
Meteorological

8. Selected drought indices
Index
Formula
Advantages
Disadvantages
Deciles of precipitation
Ascending order of deciles
of precipitation
provides an accurate statistical measurement of precipitation, easy to compute, used in region with undulating topography
accurate calculations require a long
climatic data record
Percent by normal
(Actual- Normal
/Normal)*100
Quite effective for comparing a single region or season
can’t be used for different regions
Standardized Water level Index
(Wij-Wim/)std dev)
can be computed for different time scales, detect short term droughts, less complex
Normalized Difference Vegetation Index
(IR- R/IR+ R)
provides a general measure of the state and health of vegetation, impact of climate on vegetation
Vegetation Condition Index
(NDVIj-NDVImin/NDVImax-NDVImin)
excellent ability to detect drought and to measure time of its onset, intensity, duration, and impact on vegetation
neeeds atleast 10 years of time range

9. Meteorological Drought
From 1998 to 2009 using Percent by normal
Meteorological drought = f(precipitation1, precitation precipitation)
As per Indian Meteorological Department (IMD)
Deviation ≤ -19% is No drought
Deviation ≥-19% - 59%≤ is Moderate drought
Deviation ≥ -60% is Severe drought

10. Meteorological Drought

11. Hydrological Drought
Standardized Water level Index results from 1998 to 2009
Hydrological drought = f(GW1, GW2...GWn)
Drought classes
Criterion
Extreme drought
SWI ≥ 2
Severe drought
SWI ≥ 1.5
Moderate drought
SWI ≥ 1
Mild drought
SWI ≥ 0
Non drought
SWI ≤ 0

12. Hydrological Drought
Pre monsoon
Post monsoon
Pre monsoon
Post monsoon

13. Hydrological Drought
Pre monsoon
Post monsoon
Pre monsoon
Post monsoon

14. Agricultural Drought From 1998 to 2009 using NDVI and VCI
Agricultural drought = f(vegetation1, vegetation vegetationn)

15. NDVI images
High
Low

16. Trend Adjusted VCI images
Legend
Agricultural mask
Severe drought
Moderate drought
Mild drought
No drought

17. Phase II
Data analysis
Frequency
Intensity
Chronology of drought

18. Frequency Maps
*Based on number of drought occurrence over 12 years

19. Intensity Maps
*Based on sum of deviations from the reference level

20. Chronology of drought
Results obtained from correlation between meteorological drought
Districts
With zero time lag
(Hydrological drought)
With one year lag
(Agricultural drought)
Banda
0.896
0.076
Chitrakoot
0.421
0.184
Hamirpur
0.227
0.473
Jalaun
0.592
0.168
Jhansi
0.612
0.282
Lalitpur
0.111
0.022
Mahoba
0.795
0.395
Chhatarpur
0.865
0.174
Damoh
0.037
0.416
Datia
0.727
0.303
Sagar
0.760
0.462
Panna
0.652
0.202
Tikamgarh
0.728
0.377

21. Composite Drought Risk Map
Phase III
Composite Drought Risk Map
Vulnerability Maps
Agricultural
Hydrological
Hazard Map
Meteorological

22. Hazard and Vulnerability Maps
*Product of frequency and intensity maps

23. Composite Risk Map
Composite Risk = (0.35M+0.45A+0.2H) using Multi Criteria Analysis
Where M= meteorology, A= agriculture, H= hydrology
Ranks assigned to each class
extreme=5, severe= 4, high=3, moderate= 2, mild=1

24. Conclusion Meteorological Drought = Percent by normal
Hydrological drought = SWI
Agricultural drought = NDVI and VCI
Since 1998 there has been a gradual increase in frequency and intensity of droughts
Lalitpur district is exposed to extreme hazard.
Tikamgarh, Banda, and Mahoba were the highly vulnerable to hydrological drought.
Datia, Jhansi and Hamirpur were the highly vulnerable to agricultural drought.
Composite Drought Risk = Hazard X Vulnerability
Datia, Tikamgarh, Jhansi, Mahoba and Hamirpur are at severe drought risk

25. Thanks !!