Transcript Drought Hazard and Vulnerability Analysis for Bundelkhand

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)
Objectives
3
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.
Study Area
4
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
Legend
This region faced consecutive
drought since 2004-05 to 2008-09.

M.P.
U.P.
Materials and Methods
5

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 (www.free.vgt.vito.be/)
Satellite and
Sensor
SPOT
VEGETATION
Type
S10
Instrument
VGT 1
Format
NDVI
Resolution
1 km
Region of
Interest
SE-Asia
Time Period
September
(1, 11, 21)
(1998-2009)
Materials and Methods(2)
6

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
Methodology
Phase I
Meteorological Data
Satellite Data
Hydrological Data
-Subsetting
- Agricultural
mask application
Pre-processed data
Literature Review
Meteorological drought
7
Selection of suitable indices
Agricultural drought
Hydrological drought
Methodology(2)
Phase II
Data Analysis
Frequency
Intensity
Chronology of drought
Phase III
Meteorological
Hazard Map
Agricultural
Vulnerability Maps
Composite Drought Risk Map
8
Hydrological
Selected drought indices
9
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
(NDVIjNDVImin/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
10
Meteorological Drought
From 1998 to 2009 using Percent by normal
Meteorological drought = f(precipitation1, precitation2......precipitation)
As per Indian Meteorological Department (IMD)
Deviation ≤ -19% is No drought
Deviation ≥-19% - 59%≤ is Moderate drought
Deviation ≥ -60% is Severe drought
Meteorological Drought
11
12
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
Hydrological Drought
Pre monsoon
13
Post monsoon
Pre monsoon
Post monsoon
Hydrological Drought
Pre monsoon
14
Post monsoon
Pre monsoon
Post monsoon
15
Agricultural Drought
From 1998 to 2009 using NDVI and VCI
Agricultural drought = f(vegetation1, vegetation2...... vegetationn)
NDVI images
16
High
Low
Trend Adjusted VCI images
17
Legend
Agricultural mask
Severe drought
Moderate drought
Mild drought
No drought
18
Phase II
Data analysis
Frequency
Intensity
Chronology of drought
Frequency Maps
19
*Based on number of drought occurrence over 12 years
Intensity Maps
20
*Based on sum of deviations from the reference level
Chronology of drought
21

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
22
Phase III
Meteorological
Hazard Map
Agricultural
Hydrological
Vulnerability Maps
Composite Drought Risk Map
Hazard and Vulnerability Maps
23
*Product of frequency and intensity maps
Composite Risk Map
24
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
Conclusion
25
 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
Thanks !!
26