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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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 !!