jayanta sarkar india meteorological department …during 1965 and 1966, major parts of india were...

Jayanta SarkarIndia Meteorological Department

Pune-411005India

Drought Indices in South Asia

HANOIVIETNAM

BANGKOKTHAILAND

COLOMBOSRI LANKA

SINGAPORESINGAPORE

MANILAPHILIPINES

PORT MORESBYPAPUA NEW GUINEA

ISLAMABADPAKISTAN

KATHMANDUNEPAL

YANGONMYANMAR

MALEMALDIVES

KUALA LUMPERMALAYSIA

VIENTIANELAOS

JAKARTAINDONESIA

NEW DELHIINDIA

PHNOM PENHCAMBODIA

BANDER SERI BEGAWANBRUNEI

THIMPUBHUTAN

DHAKABANGLADESH

KABULAFGHANISTAN

CAPITALCOUNTRY

SOUTH ASIAN COUNTRIES

During 1965 and 1966, major parts of India were under prolonged and severe drought conditions due to deficient monsoon rainfall. On the recommendations of the Planning commission, Drought Research Unit started functioning at Pune in 1967 in the office of the Additional Director General of Meteorology (Research)

Drought Research Unit started conducting studies on different aspects of Drought. The salient activities of this Unit are as under:-

Defining meteorological Drought and it's intensity.Delineation and identification of - Drought Prone areas of the countryStudy of past droughts andMonitoring Agricultural drought conditions during Southwest and Northeast monsoons andIssuing Crop Yield Forecasts for kharif rice and wheat crops based on statistical models

DROUGHT RESEARCH UNIT

DRU of India Meteorological Department (IMD) defines Meteorological Drought based on rainfall deficiency (SW monsoon, June-September) on sub-divisionwise basis.The meteorological Droughts are classified into

(a) moderate and (b) severe based on rainfall deficiency, i.e. 26 to 50% and more than 50% respectively.

DROUGHT RESEARCH UNIT (CONTD.)

DROUGHT MONITORING BY IMD

DRU has been deleneating sub-divisionwise drought since 1875. In our country, a year is considered to be a DROUGHT YEAR in case the area affected by moderate and severe drought, either individually or together, is 20-40% of the total area of the country and seasonal rainfall deficiency during south-west monsoon season for the country as a whole is at least 10% or more.

When the spatial coverage of drought is more than 40% it will be called as ALL INDIA SEVERE DROUGHT YEAR.(Ref.: IMD Technical Circular No. 2/2007)

The droughts over a period of 134 years (1875-2008) have been identified and classified so far.

o The drought prone areas, have been identified and probabilities of severe drought occurrences are also computed sub-divisionwise over the country.

MONITORING AGRICULTURAL DROUGHTAridity anomaly index

AI = (PE- AE)/PE* 100

(210 stations)where, AI = weekly/fortnightly aridity index

AE = Actual evapo-transpiration (thornthwaite,PE, ACTUAL RF, FC)PE = Potential evapo-transpiration (penman,TM, SOL RAD.,R.H., WIND

SPEED)For the sake of computing anomaly, long term weekly/fortnightly values of aridity index are computed. The difference between actual AI in any week/fortnight of the crop season and the normal AI expressed as a percentage called aridity anomaly which represents water stress condition. Based on the AI the incidence, spread, intensification and cessation of different drought intensities on weekly/fortnightly basis is monitored. Following criteria are used in defining various agricultural drought intensities :

Weekly/fortnightly Anomaly of AI Associated Drought Intensity1 - 25 Mild26 - 50 Moderate

>50 Severe

THORNTHWAITE’S WATER BALANCE TECHNIQUE

WATER BALANCE REFERS TO THE BALANCE BETN. WATER INCOME(PPTN.) AND LOSS OF WATER BY EVAPOTRANSPIRATION CAUSING CHANGE IN SOIL MOISTURE AND RUNOFF.IT’S A CLI. WATER BALANCE OBTAINED BY COMPARING MARCH OF PPTN. WITH EVAPOTRANSPIRATION (ET), YIELDING A NO. OF MOISTURE PARAMETERS WS, WD, SOIL MOISTURE CHANGE AND RO.

BASIC EQUN.P= ET+CHANGE IN ‘S’ +RO


o AN IMP. FEATURE OF WATER BALANCE CONCEPT:TO RECOGNISE THAT SOIL PLAYS AN IMP. ROLE IN THE EXCHANGE OF MOISTURE BETN. THE EARTH’S SURFACE AND THE ATM.

o SOIL ACTS AS A MEDIUM FOR STORING WATER (UPTO A LIMIT) DURING EXCESSIVE RF AND RELEASING THE SAME (IN A RESTRICTED MANNER) AT OTHER TIMES FOR EVAP. AND TRANSPIRATION.

o FOR WATER BALANCE COMPUTATION 3 PARAMETERS REQD.: ET (WATER NEED), P (WATER SUPPLY), AWC (AV. WATER CAPACITY,FC)


DURING THE PERIODS OF EXCESSIVE ‘RF’ THE BALANCE OF WATER, AFTER MEETING CROP DEMAND RECHARGES THE SOIL TILL ‘FC’ IS ATTAINED. ANY FURTHER ADDITION MEANS ‘RO’.‘AWC’ OF A PLACE DEPENDS ON THE TYPE OF SOIL AND THE ROOT ZONE DEPTH OF THE CROP.DURING DEFICIENT ‘RF’ ‘SM’ IS USED FOR ‘ET’PURPOSES. AS SOIL DRIES , ET RATE DECREASES. ACC. TO THORNTHWAITE, THE RELEASE OF MOSTURE FROM SOIL FOLLOWS THE FOLLOWING EQUN.:S=FC.exp –APME/FCS= Moisture remaining in the soil as storage

20.6

41.382.7143.2

140.7

73.70.30.61.22.55.110.3RO

0.00.022.2145.7

207.7

147.2

0.00.00.00.00.00.0WS

24.1

7.90.00.00.00.00.084.3116.7

98.448.733.5WD

45.6

80.3112.0

107.0

110.4

116.4

129.4

140.1

64.054.745.841.6AE

-42.0

-51.1

0.00.00.056.5133.8

-5.5-14.2

-25.1

-24.0

-28.4S*

106.9

148.9

200.0

200.0

200.0

200.0

143.5

9.715.229.454.578.5S

-125.1

-59.0

-603.9

-514.1

-383.2

-259.7

-187.0

Acc. –VE(P-PE)

-66.1

-59.0

22.2145.7

207.7

203.7

133.8

-89.8

-130.9

-123.5

-72.7

-61.9P-PE

3.629.2134.2

252.7

318.1

320.1

263.2

134.6

49.829.621.813.2P

69.7

88.2112.0

107.0

110.4

116.4

129.4

224.4

180.7

153.1

94.575.1PE

DNOSAJYJMAM FJ

LEGENDS

Acc. P-PE = ACCUMULATED POTENTIAL WATER LOSS (SUM OF –VE VALUES OF (P-PE))S= STORAGES*= CHANGE IN STORAGEWD= PE-AEWS= P-PERO= (‘WS’ of that month+’RO’ of the prev. month)/2

Standardized Precipitation Index (SPI)

A SPI-based weekly drought monitoring scheme was developed for operational monitoring of drought over India. Earlier there were some studies (Hughes and Saunders, 2002, Hayes et al. 1999, Mihajlovic, 2006) developing SPI-based drought monitoring scheme on a monthly time scale. However, it was strongly felt (Hayes et al. 1999) that developing a SPI-based drought monitoring scheme on a weekly/biweekly time scale would further improve the usefulness of SPI in drought monitoring. Keeping this in mind the SPI-based weekly drought monitoring scheme was developed for operational monitoring of drought over India.

DATA AND METHODOLOGY

For this purpose, 256 stations, spread all over the country, having a long period precipitation database of 50 years or more, were chosen. Computation of SPI involved fitting a gamma probability

density function to a given frequency distribution of precipitation totals for a station. The alpha and beta, shape and scale parameters respectively, of the gamma distribution were estimated for each station for a timescale of week for each year. Alpha and beta parameters were then used to find the

cumulative probability of an observed precipitation amount, which was then transformed into the standardized normal distribution. Thus, SPI could be said to be normalized in spaceand time scale.

The cumulative probability, after its computation, is transformed to the standard normal random variable, z with mean equal to ‘zero’ and a variance of 1, which is the value of SPI.

As SPI values fit a typical normal distribution, one can expect these values to be within ‘one standard deviation’approximately 68% of time, with ‘two standard deviations’95% of time and with ‘three standard deviations’, 99% of the time i.e. a SPI value of <-1.0 occurs 16 times in 100 year, SPI of <-2.0 occurs 2-3 times in 100 year and <-3.0 occurs once in approximately 200 years.

Advantages of the SPI

Simplicity. SPI is only based on precipitation.

PDSI complex; 68 terms are defined as part of the calculation procedures. However, main driving force in PDSI is also precipitation.

SPI versatile; can be calculated on any timescale, so suitable for both agricultural and hydrological applications. This versatility is also critical for monitoring the temporal dynamics of a drought, including its development and decline, which have always been difficult to track with other indices.

Advantages of the SPI (contd.)

As SPI values are normally distributed, the frequencies of extreme and severe drought classifications for any location and any timescale are consistent. An extreme drought occurs (according to SPI) 2-3 times in 100 years, an acceptable frequency for water management planning.

SPI not adversely affected by topography; also effective during winter months.

SPI is used in Mexico, Costa Rica, Argentina, Brazil, Chile, Turkey, Hungary, South Africa, Kenya besides USA.

Extremely DryLess than -2.0

Severely Dry-1.5 to -1.99

Moderately Dry-1.0 to – 1.49

Near Normal-0.99 to +0.99

Moderately wet1.0 to 1.49

Very wet1.5 to 1.99

Extremely wetMore than +2.0

CATEGORIESSPI

SPI - Standardized Precipitation Index

SPI, when used for monitoring drought on a weekly time scale in 2009, found to portray a realistic picture of drought scenario over the country. However, it may be mentioned that despite the current optimism about the SPI, it cannot solve all moisture monitoring concerns. Rather, it can be considered as a tool that can be used in coordination with other tools, such as the PDSI (Palmer Drought Severity Index), aridity anomaly index or remote sensing data, to detect the development of droughts and monitor their intensity and duration. This will further improve the timely identification of emerging drought conditions that can trigger appropriate responses by the central and state governments.

DROUGHT MONITORING 2009

MONSOON - 2009

Under NADAMS, agricultural conditions are monitored at district level using daily-observed coarse resolution (1.1km) NOAA AVHRR data for the entire country and at sub district level using better spatial resolution IRS AWiFS/ WiFS data.Indian Remote sensing Satellite (IRS) series (IRS 1C, IRS 1D and IRS P3) have WiFS (Wide Field Sensor) payload which collects data in red (0.62-0.68 ) and near infrared (0.77-0.86) spectral bands with spatial resolution of 188m and ground swath of 810km with a revisit period of 5 days.IRS P6 has Advanced WiFS sensor that provides spatial resolution of 56m for better monitoring of agriculture.

National Agricultural Drought Assessment and Monitoring System (NADAMS) : National Remote Sensing Centre (NRSC), India

Crop/vegetation reflects high energy in NIR band (due to its canopy geometry and health of the standing crops/vegetation) and absorbs high energy in the Red band (due to its biomass and photosynthesis).Using these contrast characteristics of vegetation in NIR and Red bands, which indicates both health and condition of crops/vegetation, NDVI is derived by the difference of these measurements and divided by their sum.

To minimize the cloud ,monthly time composite vegetation index is prepared


The monthly vegetation index map for the state with district boundaries overlaid are given in specific colours for the vegetation index ranges.The various colours in the NDVI map yellow through green to violet indicate increasing green leaf area and biomass of different vegetation types.Cloud and water are represented in black and blue colours,respectivelyBare soil, fallow and other non-vegetation categories are represented in brown colour.The seasonal progress of caompared to that of normal and complementary ground data on rainfall crop sowing progress are utilized in assessing Agricultural Drought.


NDWI indicates very low wetness in most of the taluks of the state signifying unfavourableseasonal conditions for normal progression of crop sowings.

Satellite derived agricultural vegetation condition is low throughout the state indicating that significant area is yet to be sown

•Based on anomalies of surface wetness and agricultural crop condition, it is observed that in 62 taluks the Vegetation Index and NDWI are slightly less than normal indicating agricultural drought conditions.

•Need to monitor the progression of crop condition in subsequent fortnights.

•In Aug. AWiFS images are cloudy in most parts of the state and hence the images of coarse resolution AVHRR were used for drought assessment.•Significant increase in crop condition indicating active growth of standing crops.•Increased NDVI reflect normal agricultural condition all over the state.

•Significant increase in crop condition over agricultural areas indicating active growth of standing crops.

DROUGHT : BANGLADESH, NEPAL

DURING THE LAST 50 YEARS, BANGLADESH SUFFERED ABOUT 20 DROUGHT CONDITIONS.DESPITE RECURRENT AND DEVASTATING NATURE OF DROUGHT IN BANGLADESH , IT HAS ATTRACTED FAR LESS SCIENTIFIC ATTENTION THAN FLOODS/CYCLONES.EARLIER NO STANDARD DROUGHT INDEX WAS USED FOR ASSESSMENT/MONITORING OF DROUGHT.SINCE 2009 SPI IS BEING USED(EXPERIMENTAL BASIS) TO STUDY THE SPATIAL AND TEMPORAL EXTENTS AND SEVERITY OF DROUGHT OCCURRENCE.

SPI IS MAINLY USED TO DETERMINE THE DROUGHT CHARACTERISTICS.DROUGHT EPISODES ARE REPRESENTED BY –VE SPI VALUES.DROUGHT ONSET IS DEFINED BY SPI OF –1 OR LESS. THE EPISODE ENDS WHEN THE SPI BECOMES +VE.DROUGHT DURATION IS DEFINED BY THE ONSET AND TERMINATION OF THE EVENT.THE SUM OF THE SPI VALUES DURING THE DROUGHT DURATION IS TERMED AS THE DROUGHT MAGNITUDE.

DROUGHT : MALAYSIA

DROUGHT : MALAYSIA (CONTD.)

Nieuwolt (1982) used ARI in quantifying agricultural drought in Peninsular Malaysia.

ARI is given by :

where R = monthly rainfallPE = monthly potential evapotranspiration

ARI less than 40 is considered as agricultural drought.

100)/( ×= PERARI

KEETCH BYRAM DROUGHT INDEX (KBDI)IS ALSO USED BASICALLY TO MANAGE FOREST FIRE.KBDI RELATES CURRENT AND RECENT WX CONDITIONS TO A POTENTIAL OR EXPECTED FOREST FIRE BEHAVIOUR.KBDI REPRESENTS THE NET EFFECT OF EVAPOTRANSPIRATION AND PPTN. IN PRODUCING CUMULATIVE MOISTURE DEFICIENCY IN DEEP DUFF AND UPPER SOIL LAYERS.KBDI RANGES FROM 0 TO 2000 BASED ON SOIL MOISTURE CONTENT. 0 MEANS NO MOISTURE DEFICIENCY AND 2000 IS THE MAX. DROUGHT LEVEL POSSIBLE.MAX. DAILY AIR TEMP. AND TOTAL DAILY RF ARE USED AS INPUTS.

DROUGHT : MALAYSIA (CONTD.)

CONCLUDING REMARKS

THE REVIEW OF DROUGHT INDICES HAS HIGHLIGHTED A FEW INDICES WHICH ARE CURRENTLY IN USE FOR DROUGHT MONITORING IN SOUTH ASIA.FOR DROUGHT MONITORING IN INDIA, RAINFALL DEPARTURE IS USED FOR METEOROLOGICAL DROUGHT. IN THIS REGARD, SPI, BEING DEVELOPED AND TESTED, COULD ALSO BE PROVED TO BE A GOOD INDEX. FOR MONITORING AND ASSESSING AGRIL. DROUGHT ARDITY ANOMALY INDEX IS USED. BESIDES, REMOTE SENSING APPLICATIONS COULD ALSO BE VERY EFFECTIVE IN ASSESSING DROUGHT SEVERITY, THEIR IMPACTS ON SECTORS LIKE AGRICULTURE, AND RELATED POLICY DECISIONS.

CONCLUDING REMARKS (CONTD.)

IN BANGLADESH AND NEPAL NO STANDARD METHOD WAS USED EARLIER. RECENTLY SPI IS BEING TRIED IN THESE COUNTRIES.IN MALAYSIA, SPI, AGRICULTURAL RAINFALL INDEX (ARI)AND KBDI ARE BEING USED FOR MONITORING DROUGHT, ASSESSING ITS SEVERITY AND TAKING RELEVANT POLICY DECISIONS.

THANK YOU

jayanta sarkar india meteorological department …during 1965 and 1966, major parts of india were...

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