INDICATORS OF DROUGHT MONITORING A REVIEW. Central Arid Zone Research Institute Jodhpur. K.P.R. Vittal, Amal Kar, and A.S. Rao. Drought is a normal phenomenon of earth’s climate, and a common feature in drylands D rought s everity & recurrence is maximum in arid regions
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K.P.R. Vittal, Amal Kar, and A.S. Rao
Drought is a normal phenomenon of earth’s climate, and a common feature in drylands
Drought severity & recurrence is maximum in arid regions
Meteorological, Hydrological and Agricultural drought
Socio-economic Indicators are few
Rainfall, Effective soil moisture, Surface water availability, Depth to groundwater, etc.
Vegetation cover & composition, Crop & Fodder yield, Condition of domestic animals, Pest incidence, etc.
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
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.
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.
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.
SPI, based on probability of precipitation for any time scale, is calculated as :
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.
SPI AND PEARL MILLET YIELD (Kg/ha) IN WESTERN RAJASTHAN common feature in drylands
VALUES WITHIN DISTRICTS ARE AVERAGE PEARL MILLET YIELD (Kg/ ha)
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
Surface Water Supply Index (SWSI) common feature in drylands
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.
Reclamation Drought Index (RDI) common feature in drylands
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.
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.
AGRICULTURAL DROUGHT INDICATORS common feature in drylands
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.
ARIDITY INDEX FOR INDIA common feature in drylands
16-29 JULY 2007
Moisture Adequacy Index (MAI) common feature in drylands
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.
Agricultural Drought Code Developed by CAZRI common feature in drylands
(Based on Moisture Adequacy Index)
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:
CWSI= (1-(T/Tp) SUS
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.
Comparative Performance of Agricultural Drought Indicators in Jodhpur District
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).
Table 1: Indicators of early warning systems for food security (used by major systems)
INDICATORS FOR DROUGHT EWS AND FOOD SECURITY, ESPECIALLY FOR AFRICA
Food crop performance
Crop production forecast
Marketing and price information
Food crops and shortages
Seeding risk areas
Expected season length
Estimated seeded areas
Estimated seeding date
Crop use intensity
CV of agricultural production
Cash crop production area
Av. Travel cost to nearest market
Access to water
AP3A by AGRHYMET; FIVIMS by FAO; GIEWS by FAO; SADC by Zimbabwe; FEWS by USAID; VAM by WFP
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
IN ARID AREAS
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
Thank You MONITORING