Drought Hazard and Vulnerability Analysis for Bundelkhand Region

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 q Data acquisition Ø Meteorological- Monthly precipitation data from 1998 to 2009 (Indian Meteorological Department). Ø Hydrological- Monthly data of groundwater from 1998 to 2010 for 264 stations (Central Ground Water Board) Ø Agricultural- Satellite imageries from 1998 to 2009 downloaded from (www. free. vgt. vito. be/) Satellite and Sensor SPOT VEGETATION Type S 10 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 Arc. GIS 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 Hydrological Vulnerability Maps Composite Drought Risk Map 8

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 (NDVIj. NDVImin/NDVIm ax-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(precipitation 1, precitation 2. . . 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(GW 1, GW 2. . . 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(vegetation 1, vegetation 2. . . 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. 35 M+0. 45 A+0. 2 H) 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 Ø Meteorological Drought = Percent by normal Hydrological drought = SWI Agricultural drought = NDVI and VCI Ø 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. Ø Composite Drought Risk = Hazard X Vulnerability § Datia, Tikamgarh, Jhansi, Mahoba and Hamirpur are at severe drought risk

Thanks !! 26