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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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INDICATORS OF DROUGHT MONITORING

A REVIEW

Central Arid Zone Research InstituteJodhpur

K.P.R. Vittal, Amal Kar, and A.S. Rao


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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


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Physical, Biological and Social Indicators common feature in drylands

  • 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


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METEOROLOGICAL DROUGHT INDICATORS common feature in drylands

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.


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Precipitation Departure from Normal common feature in drylands

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.


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Palmer Drought Severity Index (PDSI) common feature in drylands

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.


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Standardized Precipitation Index (SPI) common feature in drylands

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.


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SPI AND PEARL MILLET YIELD (Kg/ha) IN WESTERN RAJASTHAN common feature in drylands

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)


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HYDROLOGICAL DROUGHT INDICATORS common feature in drylands

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


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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.


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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.

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.


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AGRICULTURAL DROUGHT INDICATORS common feature in drylands

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.


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ARIDITY INDEX FOR INDIA common feature in drylands

16-29 JULY 2007

IMD, Pune


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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.


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Agricultural Drought Code Developed by CAZRI common feature in drylands

(Based on Moisture Adequacy Index)


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Crop Water Stress Index (CWSI) can be broadly divided into three major zones of agricultural drought:

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.



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DROUGHT-RELATED INDICES FROM REMOTE SENSING 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).



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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


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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


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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


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A BARE MINIMUM DROUGHT MONITORING PLAN MONITORING

  • 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.


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Thank You MONITORING


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