Long term Agriculture performance and drought vulnerability in

Long- term Agriculture performance and drought vulnerability in present and future climate in Telangana, India Presenter: Prof. P. S. Roy, NASI Platinum Jubilee Fellow, Centre for Earth and Space Sciences, University of Hyderabad, Hyderabad.

Research Question: • Will monsoonal precipitation have an impact on winter and summer agriculture performance? • Can Spatial-Temporal Analysis of Satellite Derived Vegetation Indices and Climatic Variables be used for vulnerability of droughts? Research work P. Bhavani, NASI-SRF, Ph. D Research Scholar, Reg. no: 14 ESPE 02 Centre for Earth and Space Sciences. University of Hyderabad.

Long-term 115 years rainfall anomaly 1. 5 Rainfall 1 0 -0. 5 -1 1904 1907 1910 1913 1916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 -1. 5 YEARS Rainfall anom_TS 1. 5 1 1 0. 5 0 0 -0. 5 -1 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14 78 19 76 19 74 19 19 19 72 -1. 5 70 -1 Rainfall anom_TS Extreme –ve anomaly YEARS Min. T anom Max T anom Extreme +ve anomaly Temperature Anomaly rainfall vs temperature 1. 5 19 Ø A slight increase in rainfall anomaly. Ø We notice increase in extreme negative anomalies; Ø An increase in long-term maximum temperature anomaly from 1970 onwards 0. 5 Rainfall Long- term Climate Anomaly in Telangana R 2 = 0. 0071

• Drought in India has resulted in tens of millions of deaths over the course of the 18 th, 19 th, and 20 th centuries. • Telangana agriculture is heavily dependent on the climate of India: a favorable southwest summer monsoon is critical in securing water for irrigations and rain fed crops; • In 67% of agriculture area in India is rain-fed (63% in Telangana), hence failure of the monsoons result in below-average crop yields. Source : https: //en. wikipedia. org/wiki/Drought_in_India Source: http: //www. civilsdaily. com/blog/indian-agriculture-104 -everythingthat-you-need-to-know-about-drought-management-in-india/

Study Area: Cropping seasons Summer monsoon/ June -September Winter/ October. January Summer/ February. May

Data used & Methodology Satellite MODIS NDVI 2001 -2015 Non-agriculture mask Hierarchal classification method Climate data GIMMS NDVI 1982 - 2000 Stacking of three cropping season NDVI Extraction crop/non crop Dev NDVI & VCI 1982 - 2000 Irrigation data • • 2000 -2015 • Precipitation; Maximum Temperature; Minimum Temperature. Net irrigation Surface water irrigation Ground water irrigation Stacking of three cropping season Precipitation Max Temp Min Temp Simple & Multiple linear regression SPI SM NDVI W NDVI Crop Area (%) S NDVI Ratio Crop Area (%) Fluctuation 1982 -2000 Agriculture drought condition Frequency and Magnitude of Drought Driving Parameter of NDVI versus rainfall & irrigation sources 2000 -2015 Crop stress/ drought condition SM: Summer monsoon; W: Winter season; S: Summer season; NDVI: Normalized Differential Vegetation Index; VCI: Vegetation Condition Index; Dev NDVI: Deviation of NDVI; Max Temp: Maximum Temperature; Min Temp: Minimum Temperature

Agricultural drought assessment and Crop stress performed using following indices: 1. Conventional method to assess the drought/vegetation condition. 2. Percentage of deviation of NDVI derived of the agricultural area to assess the frequency of stress 3. Assess the percentage of potential cultivable area 4. Capture the sensitivity of crop stress variability Note: Vegetation condition Index (VCI) captures the only maximum vegetation stress condition. 95% of significant relation exist with deviation of NDVI.

1 st objective Results: The major results presented are in following section. Ø 1982 -2000 (GIMMS NDVI): • Seasonal pattern of GIMMS NDVI with precipitation of undivided state (TSAP). Ø 2000 -2015 (MODIS NDVI) • Seasonal ratio of crop fluctuation (%) at district wise. • Signification relation of agricultural performance with precipitation and irrigation sources. • Spatial distribution of crop performance. Article published: Bhavani P; Chakravathi V; P S Roy et al. 2016. Long-term agricultural performance and climate variability for drought assessment: A regional study from Telangana and Andhra Pradesh states, India. Geomatics, Natural Hazards and Risk.

Deviation of NDVI vs SPI 0. 05 1 0. 03 0 -0. 01 -0. 5 -0. 03 -1 -0. 05 -1. 5 19 8 YEARS 100 S Dev. NDVI 80 82 80 SM_SPI W SPI S SPI 80 77 56 51 60 35 40 20 24 25 23 36 25 8 21 7 25 29 86 16 42 S RCF% l ga an ar W Ra ng ar ee ba am N iz DISTRICT SM RCF % W RCF% dy d a nd go N al ed M ub ab ah ak . . . na am m K ha m M K ar im an la b ag ar ad 0 di • These fluctuations are primarily due to the variation in precipitation and availability of soil moisture (Komuscu et al. 1999; Mahmudul Alam et al. 2011 and Yinhong Kang et al. 2009). W Dev. NDVI Ratio of Crop Area Fluctuation A • The maximum observed fluctuation at the state level is 79%, during the summer monsoon, followed by summer (57%) and winter (7%). % Ratio of Crop area Fluctuation SM Dev. NDVI SPI 0. 5 0. 01 283 83 -8 4 84 -8 5 85 -8 6 86 -8 7 87 -8 8 88 -8 9 89 -9 0 90 -9 1 91 -9 2 92 -9 3 93 -9 4 94 -9 5 95 -9 6 96 -9 7 97 -9 8 98 -9 9 99 -0 0 Dev NDVI • During the summer monsoon, the deviation of the NDVI closely follows the pattern of the corresponding SPI except during 1999– 2000, where an abrupt raise in the NDVI is noticed from August to because of the impact of rainfall. • During winter and summer the NDVIDev follows the pattern of monsoons SPI 1. 5

Yearly seasonal relation of NDVI vs rainfall and water resources: • During summer monsoon, the state NDVI showed a 95% (p<0. 05) significant relation with the summer monsoon rainfall in most of the years, except 2001– 2002, 2009 -2010 and 2014 -2015. • There was no significant relation normal and best monsoonal years. • Winter crop season, shows a 95% significant relationship with monsoonal precipitation. • Except the drought years with addition irrigation sources. • Summer cropping periods, depends on the total monsoon precipitation and irrigation facility. • Years R 2 Monsoon Remarks Rain Winter R 2 2000 -2001 Remarks Summer Remarks Multiple R 2 Multiple Regression WNDVI vs MR and WR 0. 79* WNDVI vs MR and WR and NIA 0. 46* 0. 40* 0. 79* SNDVI vs 0. 69* Annual 0. 65* Total Rain 0. 68* and NIA 0. 50* WNDVI vs MR 0. 67* and WR and SW 0. 27 0. 59* 0. 87* 0. 17*** 0. 44*** 0. 56* 0. 39** 2003 -2004 -2005 -2006 0. 55* 0. 97* 2006 -2007 -2008 -2009 -2010 -2011 -2012 -2013 -2014 -2015 0. 36* 0. 67* 0. 06 0. 20 0. 53* 0. 81* 0. 17*** 0. 04 0. 05 0. 70* 0. 64** 0. 37* 0. 85* 0. 70* 0. 21* 0. 86** 0. 68* 0. 01 0. 22 0. 19*** 0. 69* 0. 08 2001 -2002 -2003 0. 74* 0. 53* MNDVI vs MR 0. 89* 0. 44* *: 95 % confidence level (p<0. 05), **: 90 confidence level (p<0. 10), *** : 85%; No significant MNDVI: Monsoon NDVI, WNDVI : Winter NDVI, SNDVI: Summer NDVI, MR: Monsoon Rain, WR; Winter Rain, SW: Surface Water NIA: Net Irrigated Area.

SM Crop area W Crop Area b) a) a) b) b) c) c) SM Rain 5 01 -2 14 20 -2 01 4 3 01 20 9. 00 8. 00 7. 00 6. 00 5. 00 4. 00 3. 00 2. 00 1. 00 0. 00 W Rain S Rainfall (mm) b) 13 -2 12 20 -2 01 2 1 01 11 -2 10 S Crop Area 20 YEARS 20 20 09 -2 01 0 9 00 8 -2 20 08 00 7 20 07 -2 00 6 20 06 -2 00 5 20 05 -2 00 4 20 04 -2 00 3 20 03 -2 00 2 -2 00 02 20 -2 00 01 -2 20 00 20 a) 2000 -2015 Seasonal pattern of Cropped area (%) and rainfall 90 80 70 60 50 40 30 20 10 0 1 % Cropped area Spatial and temporal distribution of extreme & severe dry; and normal & extreme good crop area years during 2000 -2015 (a) summer monsoon; (b) winter; and (c) summer season. a)

2 nd objective : Trend analysis of NDVI vs Climate (Rainfall, Maximum Temperature) and soil moisture NDVI vs Rainfall NDVI vs Maximum Temperature NDVI vs Soil Moisture Parameters NDVI Max Temperature -0. 55 Rainfall 0. 47 Soil Moisture 0. 61 Correlation R values

Length of the Growing Period (LGP) vs Crop Production 21000 150 16000 11000 100 6000 1000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 YEARS Tot. Area Prod TS LGP TS 50 LGP Production tonnes/hec Length of the Growing Period(LGP) vs Rainfall and crop production 26000

Vulnerability : • IPCC defines vulnerability (V) as the composite index of exposure (E), sensitivity (S) and adaptive index (AC). • Exposure (E): “the nature and degree to which a system Exposure is exposed to significant climatic variation” (IPCC, 2001); Vulnerabil ity • Sensitivity (S): “degree to which a system is affected, either adversely or beneficially, by climate variability or change” (IPCC, 2007); and • Adaptive Capacity (AC): “the ability (or potential) of a system to adjust successfully to climate change” (IPCC, 2007). Sensitivity Adaptive Capacity

IPCC AR 5 (2014): • IPCC Fifth Assessment Report (AR 5) relies on the Coupled Model Intercomparison Project Phase 5 (CMIP 5), an Time Periods international effort among the climate modeling community to coordinate climate change experiments. 2050 (average for 20412060) 2070 (average for 20612080) RCP Peak of GHG emission (measured in CO 2 equivalents) concentrations reaching 421 ppm (RCP 2. 6), 538 ppm 2. 6 2010 -2020, then decline (RCP 4. 5), 670 ppm (RCP 6. 0), and 936 ppm (RCP 8. 5) by the 4. 5 Peak around 2040, then decline 6. 0 Peak around 2080, then decline 8. 5 Continue to rise throughout the 21 st century • Most of the CMIP 5 and Earth System Model (ESM) simulations for AR 5 WRI were performed with prescribed CO 2 year 2100. (IPCC AR 5 WGI, ). • The numbers refer to radiative forcings (global energy imbalances), measured in watts per square metre, by the year 2100. Note: For present study 2050 (Had. GEM 2 -ES) RCP 2. 6 Scenarios (30 seconds) data is considered. Source: https: //en. wikipedia. org/wiki/Representative_Concentration_Pathways; http: //www. worldclim. org/cmip 5_30 s,

Satellite 1982 - 2015 1982 -2015 • • • No DF; 2000 -2015 %RCAF; VCI -ve relation with vulnerability Future AR 5 Scenarios • • Exposure So. Er; AWHC NSA; GCA. Components of Vulnerability Identification of Indicators Sensitivity Adaptive Capacity • Ranking of Indicators • Normalisation • AHP Generated for 2030 2011 Precipitation; Maximum Temperature; Minimum Temperature. +ve relation with vulnerability Socio-economic data Field data Climate data • • • • Pop. D; T-Ag. L; % Mg. R; Ag. W; Ag. P; L/S; %Li. R; % Li. T; TIA; GIA; Ag. P Ag. Cr. So Com. B RN Ag. Mg. So Agriculture Drought Vulnerability ADVI =Exposure + Sensitivity. Adaptive Capacity • • GIA; L/S; Ag. W; %Li. R; %Li. T; T-Ag. L; Pop. D rk wo e m a Fr 07) C IPC (20

Parameters used for agricultural drought vulnerability analysis for three cropping periods Current 1982 -2015 • • • Exposure Future (2050 & 2070) GIS Analysis Adaptive Capacity District Mandal 2000 -2015 Precipitation RCP 2. 5 Maximum Temperature RCP 4. 5 Minimum Temperature RCP 6. 0 RCP 8. 5 Analysis Sensitivity District Mandal • • • Precipitation Maximum Temperature Minimum Temperature • • • GCA No. DF % RCF VCI So. Er AWHC Pop. D T-Ag. L % Mg. R • • • NSA GCA % RCF VCI % NCA So. Er AWHC Pop. D TAg. L • • G-Ir Ex. G-Ir Ag. W Ag. P L/S %Li. R % Li. T • • TIA Ag. P Ag. Cr. So Com. B %Li RN Ag. Mg. So Extraction of pre-processing Indicators Normalisation of Indicators Weights of indicators using AHP method Agricultural Drought Vulnerability index for all climate scenarios ADVI= ([E+S]-AC) Overlay of district/mandal boundary to find the highest vulnerable zones Generate vulnerability map No. DF: No. of Drought Frequency; %RCF: Percentage Ratio of Crop Fluctuation; VCI: Vegetation Condition Index; GCA: Gross Cropped Area; S. Er: Soil Erosion; AWHC: Available water holding Capacity; Pop. D: Population Density; T-Ag. L: Total Agriculture Labour; % Mg. R: Percentage of Migrants Rural; % NCA: % Non cultivated area; %NSA: Percentage of Net Sown Area; %Li. T: Percentage of Total Literacy; %Li. R: Percentage of Rural Literacy; Ag. P: Agriculture Power Consumption; Ag. W: Agriculture Wages; L/S: Live Stock; Ex. G-Ir: Extent of Gross Irrigated Area; G-Ir: Gross Irrigated Area; Ag-Cr. So: Agriculture Credit Society; Co. B: Commercial Banks; RN: Road Networks; Ag-Mr. So: Agriculture Marketing Society; RCP: Representative Concentration Pathway.

3 rd Objective Results: The results presented are in following section. Spatial distribution of Adaptive Capacity; Sensitivity; Exposure; and Vulnerability for three cropping seasons. Ø Recent-past status (1982 -2015) § District level; and § Mandal level. Ø Future climate scenario (2050, RCP 2. 6 Scenarios): § District level Communicated: Bhavani P, P K Joshi, Chakravathi V et al. A comprehensive agriculture drought vulnerability monitoring approach integrating satellite, climate and socio-economic data.

• Majority (80%) of the total geographical area shows very less to less adaptive capacity. • During summer monsoon, 12. 5% of area from total geographical area is found to be very high to high vulnerable. • During winter season, 39 % of area from total geographical area is found to be very high to high vulnerable. • During summer season, 23. 4 % of area from total geographical area is found to be very high to high vulnerable. Recent-past (1982 -2015): Spatial distribution • Due to the impact of monsoonal of AC, S, E, ADVI at district level. climate variation, winter season shows highest area vulnerability, followed summer season. of by SM: Summer Monsoon; W: Winter; S: Summer SM W S

• Majority of mandals of coastal districts found moderate to very less adaptive capacity. SM • During summer monsoon, , Mahbubnagar, Nalgonda and Adilabad shows 25 -30 % of mandals from total mandals are very high to high vulnerable. W • Highest % of mandals of Mahbubanagar and Adilabad districts are very high to high during winter season. S Spatial distribution of AC, S, E, ADVI at • Majority of mandals of Mandal level. Nalgonda district are very high to high vulnerable.

• During summer monsoon, approximately 10% increase in vulnerability area, compare to present vulnerability. SM • During winter season, 23. 4 % of area from total geographical area is found to be very high to high vulnerable. • During summer season, 12. 5 % of area from total geographical area is found to be very high to high vulnerable. • The raise in % of area vulnerable found during winter and summer monsoon. W Future Scenario-2050 RCP 2. 6: Spatial distribution of AC, S, E, ADVI at district level. SM: Summer Monsoon; W: Winter; S: Summer S

a) Drought Year 1 7 2 7 9 3 6 4 2050 RCP 2. 6 1 1 5 8 Projected agriculture NDVI Good Year Normal Year 7 2 9 5 8 2 7 9 5 3 6 1 8 3 6 4 4 2 9 5 8 3 6 4 NDVI b) 1 2 7 7 9 5 8 1 1 9 5 3 6 8 4 7 2 3 6 1 7 9 5 8 4 7 6 3 8 4 3 6 7 2 9 6 3 4 9 8 3 6 4 1 7 2 9 5 8 2 5 1 5 8 9 5 1 2 7 2 4 4 c) 1 6 3 2 9 5 8 6 3 4 1: Adilabad; 2: Kharimnagar; 3: Khammam; 4: Mahbubnagar; 5: Medak; 6: Nalgonda; 7: Nizamabad; 8: Rangareddy; and 9: Warangal a)Summer Monsoon; b) Winter season; and c) Summer season

Conclusion: • The study highlights the long-term (1982– 2015) inter-annual variation in the NDVIDev, SPI and cropped areas (2000– 2015). • The vulnerability of the region, it is essential to use the satellite derived indices (NDVIDev), the cropped area and its fluctuation, the preceding year’s seasonal precipitation and the sources of irrigation. • The affect of vulnerability is high during winter and summer seasons found to be high. This assessment is on district level. • Future climate (2050) shows decline in agriculture NDVI and increase in agriculture drought vulnerability during summer monsoon period. • In all climate change scenarios of IPCC, the intensity of agriculture drought vulnerability will significantly increase. This significant finding can help in taking apriori steps for adaptation and mitigation.

Long term Agriculture performance and drought vulnerability in present and future climate in Telangana, India.