drought hazard and vulnerability a nalysis for bundelkhand region using geo-spatial tools
DESCRIPTION
Drought Hazard and Vulnerability A nalysis for Bundelkhand Region using Geo-Spatial Tools. Supervisors (s) : Dr . Anil K. Gupta, Ms Sreeja S. Nair, (NIDM), Dr. V. K. Sehgal, (IARI) Dr . P. K. Joshi, (TERI University). Anjali Singh, SRF, Indian Agricultural Research Institute (IARI). - PowerPoint PPT PresentationTRANSCRIPT
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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)
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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.
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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 2004-05 to 2008-09.
U.P.
M.P.
Legend
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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
(www.free.vgt.vito.be/)Satellite and
Sensor TypeInstrumen
tFormat
Resolution
Region of Interest
Time Period
SPOT VEGETATION
S10 VGT 1 NDVI 1 km SE-AsiaSeptember(1, 11, 21)(1998-2009)
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Materials and Methods(2)
Softwares used
Software Utility
ENVI 4.4For image processing, district mask generation, agricultural mask application, NDVI and VCI computation
ArcGIS 9.1For districts vector and raster file preparation, interpolation (surface layer creation) and maps preparation
Microsoft Excel (2007)
For data arrangement and using other calculations
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-Subsetting
- Agricultural mask application
Meteorological Data
Satellite DataHydrological
Data
Pre-processed data
Selection of suitable indices Literature Review
Meteorological drought
Agricultural drought
Hydrological drought
Phase I
Methodology
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Phase II
Phase III
Methodology(2)
Composite Drought Risk Map
Vulnerability Maps
Agricultural Hydrological
Hazard Map
Meteorological
Data Analysis
Frequency Intensity Chronology of drought
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Selected drought indices
Index Formula Advantages Disadvantages
Deciles of precipitation
Ascending order of decilesof precipitation
provides an accurate statistical measurement of precipitation, easy to compute, used in region with undulating topography
accurate calculations require a longclimatic 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
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From 1998 to 2009 using Percent by normalMeteorological drought = f(precipitation1, precitation2......precipitation)
Meteorological Drought
As per Indian Meteorological Department (IMD)
Deviation ≤ -19% is No drought
Deviation ≥-19% - 59%≤ is Moderate drought
Deviation ≥ -60% is Severe drought
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Meteorological Drought
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Standardized Water level Index results from 1998 to 2009 Hydrological drought = f(GW1, GW2...GWn)
Hydrological Drought
Drought classes Criterion
Extreme drought SWI ≥ 2
Severe drought SWI ≥ 1.5
Moderate drought SWI ≥ 1
Mild drought SWI ≥ 0
Non drought SWI ≤ 0
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Pre monsoon Post monsoon Pre monsoon Post monsoon
Hydrological Drought
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Pre monsoon Pre monsoonPost monsoon Post monsoon
Hydrological Drought
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From 1998 to 2009 using NDVI and VCI
Agricultural drought = f(vegetation1, vegetation2...... vegetationn)
Agricultural Drought
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NDVI images
High
Low
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Trend Adjusted VCI images
Agricultural maskSevere drought
Moderate droughtMild drought
No drought
Legend
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18 Phase II
Data analysis
Frequency Intensity Chronology of drought
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Frequency Maps
*Based on number of drought occurrence over 12 years
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Intensity Maps
*Based on sum of deviations from the reference level
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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 0.076Chitrakoot 0.421 0.184Hamirpur 0.227 0.473Jalaun 0.592 0.168Jhansi 0.612 0.282Lalitpur 0.111 0.022Mahoba 0.795 0.395Chhatarpur 0.865 0.174Damoh 0.037 0.416Datia 0.727 0.303Sagar 0.760 0.462Panna 0.652 0.202Tikamgarh 0.728 0.377
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Composite Drought Risk Map
Vulnerability Maps
Agricultural Hydrological
Hazard Map
Meteorological
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Hazard and Vulnerability Maps
*Product of frequency and intensity maps
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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 classextreme=5, severe= 4, high=3, moderate= 2, mild=1
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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
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Thanks !!