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)

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.

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. Legend M.P. U.P.

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 Type Instrument Format Resolution Region of Interest Time Period SPOT VEGETATION S10 VGT 1 NDVI 1 km SE-Asia September (1, 11, 21) (1998-2009)

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

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

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

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

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

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 Post monsoon Pre monsoon Post monsoon

Hydrological Drought Pre monsoon Post monsoon Pre monsoon Post monsoon

Agricultural Drought From 1998 to 2009 using NDVI and VCI Agricultural drought = f(vegetation1, vegetation2...... vegetationn)

NDVI images High Low

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

Phase II Data analysis Frequency Intensity Chronology of drought

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

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

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

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

Hazard and Vulnerability Maps *Product of frequency and intensity maps

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

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

Thanks !!