Central Arid Zone Research Institute Jodhpur

1. INDICATORS OF DROUGHT MONITORING A REVIEW Central Arid Zone Research InstituteJodhpur K.P.R. Vittal, Amal Kar, and A.S. Rao

2. Drought is a normal phenomenon of earth’s climate, and a common feature in drylands Drought severity & recurrence is maximum in arid regions • Compels Govt to spend huge amount in relief and rehabilitation • Yet, lack of proper assessment and warning systems lead to confusion or delay in reaching affected people/region • Needs development of robust assessment and monitoring tools • Indicators are available for measuring: Meteorological, Hydrological and Agricultural drought Socio-economic Indicators are few • None of the current indicators are universally acceptable • Also, not all is suitable for every region • Arid regions need a set of robust indicators and assessment tools for their high vulnerability

4. Physical, Biological and Social Indicators • Physical indicators include Rainfall, Effective soil moisture, Surface water availability, Depth to groundwater, etc. • Biological/ Agricultural indicators comprise Vegetation cover & composition, Crop & Fodder yield, Condition of domestic animals, Pest incidence, etc. • Social indicators are mostly impact indicators and include Food and Feed availability, Land use conditions, Livelihood shifts, Migration of population, etc. In most cases only those indicators that measure the rainfall needs of following sectors are considered: (a) agricultural need, (b) drinking water supply, and (c) storage of reservoirs and ground water

7. METEOROLOGICAL DROUGHT INDICATORS Deciles of Precipitation (DI) Monthly precipitation totals from a long-term record (~30 years) are used for deciles, which are grouped further into five precipitation classes : 0-20% much below normal 20 to 40% below normal 40 to 60% near-normal 60 to 80% above-normal 80 to 100% much above normal DI is used widely in Australia for drought relief programme. Merit : DI is simple to calculate, requires only precipitation data and fewer assumptions. Demerit: Too simplistic to inform about gravity of the problem in different sectors.

8. Precipitation Departure from Normal IMD describes meteorological drought from rainfall departure from its long term averages and declares meteorological drought on weekly/monthly basis. Departure of annual rainfall from normal (%) 0 or above No drought 0 to –25 Mild drought -26 to –50 Moderate drought -50 or more Severe drought When >50% area of the country gets moderate or severe drought, the country becomes severely drought-affected; if 26-50% area is affected, country becomes moderately drought-affected. Merit : Simplicity makes this index popular in India. Demerit : Average precipitation is not always the same as median precipitation. Also, distribution or time-scale of rainfall is not specified.

9. Palmer Drought Severity Index (PDSI) PDSI, popular in the US, uses data on precipitation, temperature and local available water content (AWC) of soil, and calculates the difference between Climatically Appropriate For Existing Conditions (CAFEC) rainfall and actual rainfall as a drought indicator. PDSI generally varies between -4.0 (extreme drought) and +4.0 (adequate moisture condition). Drought categories are: Index value Class for drought - 1.00 to –1.99 Mild drought - 2.00 to –2.99 Moderate drought - 3.00 to - 3.99 Severe drought < - 4.00 Extreme drought Merit :PDSI quantifies abnormality of weather in a region, including in historical past. It can well be used for spatio-temporal variability of drought. Demerit : The index values did not often match the situation in India.

11. Standardized Precipitation Index (SPI) SPI, based on probability of precipitation for any time scale, is calculated as : X -Xm SPI = -----------  Where X = Precipitation for the station Xm = Mean precipitation  = Standardized deviation SPI Drought Classes Less than -2.00 Extreme drought -1.50 to -1.99 Severe drought -1.00 to -1.49 Moderate drought -0.99 to -0.00 Mild drought Merits :Can be computed for different time scales Can provide early warning of drought Can help assess drought severity Is less complex than PDSI Demerits:Groundwater, stream flow, and reservoir storage reflect longer-term precipitation anomalies. So, SPI is calculated for 3, 6, 12, 24, and 48 month time scales.

13. SPI AND PEARL MILLET YIELD (Kg/ha) IN WESTERN RAJASTHAN 224 1137 946 76 866 18 152 118 986 1404 286 1051 126 583 197 1218 70 1166 7 552 753 0 158 1259 VALUES WITHIN DISTRICTS ARE AVERAGE PEARL MILLET YIELD (Kg/ ha)

14. HYDROLOGICAL DROUGHT INDICATORS Groundwater and Reservoir Level Monitoring of all reservoir water levels and groundwater table through a closed well observation network is important. Standardized Water level Index(SWI) An index based on water level probability for any time scale. SWI= (Wij –Wim)/ where, Wij is the seasonal water level for ith and jth observation, Wim its seasonal mean, and  is its standard deviation. Merits:SWI can be computed for different time scales Can provide early warning of water storage Can help in assessing hydrological drought severity

15. Surface Water Supply Index (SWSI) Designed for river basins with a component of mountain snow input.Integrates reservoir storage, stream flow and snow and rain into a single index. where a, b, c, and d are weights for snow, rain, stream flow and reservoir storage, respectively; while (a+b+c+d) = 1, and Pi = probability (%) of non-exceedence for each of the four water balance components. Calculated at monthly time step. Demerits :Unique to each basin or region, so difficult to compare across basins or regions. Changes in water management in a basin, necessitates redevelopment of the algorithm. Extreme events cause a problem if events surpass historical time series.

16. Reclamation Drought Index (RDI) RDI is calculated at river basin level. Inputs: temperature, precipitation, snow pack, stream flow, reservoir level. Impetus came from the Reclamation States Drought Assistance Act of 1988 in the USA, for seeking drought assistance. RDI Classification 4.0 or more Extremely wet 1.5 to 4.0 Moderately wet 1.0 to 1.5 Normal to mild wet 0.0 to -1.5 Normal to mild drought -1.5 to -4.0 Moderate drought -4.0 or less Extreme drought RDI is similar to SPI, PDSI, and SWSI. Merit :Builds a temperature-based demand component and a duration into the index. Can account for both climate and water supply factors. Demerit :Index is unique to each river basin, so inter-basin comparison is limited.

17. AGRICULTURAL DROUGHT INDICATORS Aridity Index Aridity Index indicates water-deficit conditions in a region. Crop-water requirements are not considered. Calculated as percentage ratio of annual water deficit to annual water need or annual potential evapo-transpiration (PE). Aridity anomaly (Ia) indexis the percent departure of the anomaly value from the normal.IMD monitors Ia during kharif seasonfor the country as a whole and during rabi season for areas receiving NE monsoon rains. Drought Category Anomaly Value Mild drought Up to 25% Moderate drought 26-50% Severe drought > 50% Demerit:Although simple, water balance calculations do not properly account for rainfall-runoff before the stored moisture is estimated.

18. ARIDITY INDEX FOR INDIA 16-29 JULY 2007 IMD, Pune

19. Moisture Adequacy Index (MAI) CAZRI developed MAI for quantification of agricultural drought, which is defined as : MAI = AE/PE where AE is actual evaporation, and PE potential evapo-transpiration (in %) during different phonological stages of a crop. MAI is obtained from weekly water balance. Drought impact is related to moisture availability at certain crop growth stages. Hence, categories of MAI (severity) at different growth stages are integrated into a single index value to identify drought impact on a particular crop. Merit: Water balance calculation takes into account soil characteristic, crop growth period and water requirement of major crops. Drought is specified crop-wise on a real- time basis. Demerit: Calculations are data-intensive, and hence difficult to implement under data-scarce conditions.

20. Agricultural Drought Code Developed by CAZRI (Based on Moisture Adequacy Index)

21. Based on the criteria developed by CAZRI, western Rajasthan can be broadly divided into three major zones of agricultural drought: • Zone of Maximum Occurrence (1 in 2-3 Y); • Zone of Medium Occurrence (1 in 3-4 Y); • (3) Zone of Fewer Occurrence (1 in 4-5 Y)

22. Crop Water Stress Index (CWSI) CWSI values are a daily integration of plant-available soil water, evaporative demand and plant phenological stage susceptibility, and is defined for the growing season as: Harvest CWSI=  (1-(T/Tp) SUS Planting where, T is the computed actual transpiration (mm/day), Tp is potential transpiration (mm/day) and SUS is seasonally dependent weighting factor for grain yield susceptibility. SPAW model is used for simulation of soil water and calculation of effective rainfall for plant transpiration. Merits :The estimates using dynamic simulation models are reasonably good. Demerits: SPAW model needs calibration for each crop and region and hence has a limitation for use.

24. Comparative Performance of Agricultural Drought Indicators in Jodhpur District

25. DROUGHT-RELATED INDICES FROM REMOTE SENSING Major Indices: NDVI, EVI, VCI, TVI, etc. Normalized Difference Vegetation Index (NDVI) where  is reflectance in the near-infra-red (NIR) and red (red) band of satellite sensor, respectively. NDVI ranges from -1 to 1. Drought severity is evaluated as difference between NDVI for current month (e.g. September 2007) and a long-term (30-year-long) mean NDVI for the month. Since 1989 NADAMS is providing bi-weekly drought bulletins for kharif season at district level in India, based on satellite-derived greenness of plant cover. Merits :Calculation simple; daily satellite data available; several sensor wavelengths and calculation options now available Demerits :Persistent cloud cover during monsoon Misrepresentation in sparsely vegetated areas Often lagging actual occurrence by weeks to month Does not yet reliably quantify biomass, crop condition, grain yield or even plant density CAZRI’s experience with PD-54 index (Australia) for rangeland vegetation was fruitful than NDVI. The PD-54 was later improved as SAVI (soil adjusted vegetation index) and later modified (MSAVI).

26. Experience from CAZRI’s Weather-based Agro-advisory Services to Farmers

28. Table 1: Indicators of early warning systems for food security (used by major systems) INDICATORS FOR DROUGHT EWS AND FOOD SECURITY, ESPECIALLY FOR AFRICA Indicator AP3A FIVIMS GIEWS SADC FEWS VAM Food crop performance      Crop conditions      Crop production forecast    Marketing and price information      Food supply/demand      Health conditions   Food crops and shortages     Food supply   Food consumption   Crop areas       Pests   Food balance      Vegetation front  CCD      NDVI      Biomass  Seeding risk areas  Expected season length    Estimated seeded areas   Estimated seeding date  Vegetation cover Agro-ecological zones  Crop use intensity  CV of agricultural production  Cash crop production area     Coping strategies  Av. Travel cost to nearest market  Livestock production  Population density      Access to water  Children’s education  Rainfall      AP3A by AGRHYMET; FIVIMS by FAO; GIEWS by FAO; SADC by Zimbabwe; FEWS by USAID; VAM by WFP

29. NEED FOR A GIS-BASED DECISION SUPPORT SYSTEM TO MEASURE, MONITOR, WARN ABOUT AND MANAGE DROUGHT ‘Drought’ has DIFFERENT CONNOTATIONS and CONTEXTUAL RELATIONS in different areas and societal segments of India Consequently, NO ONE SET OF INDICES may provide a full glimpse of the problem in the country as a whole Largest segment of society affected by drought in India depends on AGRICULTURE Within agriculture sector, CROP CULTIVATORS ARE MOST VULNERABLE CAZRI’s STUDIES onIMPACT OF 2002 DROUGHT showed that MEDIUM & SMALL FARMERS have MAXIMUM VULNERABILITY Marginal Farmers and affected Weaker Section of the societyGET PRIORITYinSOCIAL SECURITY COVERduring drought relief Next most-vulnerable appeared to be the LIVESTOCK RAISERS who migrate with large herds of animals DOMINANTLY RAIN-FED AGRICULTURE VILLAGES WITH POOR ACCESSto roads and other infrastructures constitute the MOST VULNERABLE AREAS TRADITIONAL WISDOM IN DROUGHT MANAGEMENT is getting eroded due toOVER-DEPENDENCE ON DROUGHT RELIEF

30. MINIMUM DATA LAYERS FOR A DSS ON DROUGHT VULNERABILITY & MONITORING IN ARID AREAS CLIMATELAND RESOURCESSOCIO-ECONOMIC LANDFORM POPULATION STRUCTURE RAINFALL SOIL TEXTURE OCCUPATION STRUCTURE TEMPERATURE SOIL DEPTH INFRASTRUCTURE LIVESTOCK COMPOSITION SOIL MOISTURE TREE/SHRUB COVER MARKET ACCESS CROPS GROWN TRANSPORT NETWORK IRRIGATION WATER AVAILABILITY DATA SOURCES REAL-TIME GROUND INFORMATION & SATELLITE PRODUCTS (incl. MICROWAVE), SECONDARY INFORMATION, SAMPLE SURVEY SOME KEY WORDS FOR MODELLING MOISTURE AVAILABILITY, FOOD AND FEED AVAILABILITY, VULNERABLE AREAS & GROUPS, DRINKING WATER, HUMAN & LIVESTOCK HEALTH, MIGRATION ROUTES, LIVELIHOOD OPTIONS, VILLAGE ACCESSIBILITY, TRADITIONAL ASSET CONDITION

31. A BARE MINIMUM DROUGHT MONITORING PLAN • Step 1: Collection of rainfall and temperature data from different locations (In collaboration with IMD and State agencies) • Step 2: Calculation of temporal and spatial availability of soil moisture in a GIS environment using pre-calibrated dynamic simulation models for all major crops taking into water requirement and soil characteristics. • Step: To find out threshold limits for each crop as a warning as no drought or mild, moderate and severe drought conditions for each location. • Step 4: Taking medium range forecasting, preparation of early drought warning bulletins on drought status and disseminate to drought managers. • Step 5: Preparation of Agro-advisory bulletins based on drought conditions and contingency plans in case of late onset of monsoon. • Step 6: Dissemination of Agro-advisory bulletins to farmers through local media and get feed back.

32. Thank You