/

Agricultural Water Management 98 (2011) 1373-1387

Contents lists available at ScienceDirect

Agr~cultural W~tef ~anag~ment .- . '.:! .

j OU rn a I h 0 me p ag e: w'~'~ :~ I s e"vie.r .cci~11 oc ate/ ~gwa t ELSEVIBR

Extreme rainfall indices and its impact on rice productivity-A case study over sub-humid climatic environment

N. Subasha,b,*, S.S. Singh a, Neha Priya a

'/CAR Research Complex/or Eastern Region, /CAR Parisar. Bihar Veterinary College P.O., Pama 800014, Bihar, India b Project Directorate for Farming Systems Research (/CAR}, Modipuram, Meerut 250170, Uttar Pradesh, India

ARTICLE INFO

Arricle history: Received 21 October 201 O Accepted IS April 2011 Available online 10 May 2011

Keywords: Extreme rainfall indices Mann- Kendall nonparametric test Trend analysis Agro-climatological zone Productivity anomaly index Correlacion

1. lntroduction

ABS T RACT

Frequent occurrences of extreme rainfall events create severe threat to agricultural production. This is one of the most significant consequences of global warming due to increase in greenhouse gases. A precise understanding of frequency and magnitude of these events and its influence on agricultural productivity can extensively help in policy decisions and planning agricultural as well as water management opera­tions. This study has analyzed observed trends in extreme rainfall indices during monsoon months as well as seasonally at four stations located over different agro-ecological zones of Bihar. namely Samastipur (zone-I), Madhepura (zone-II). Sabour (zone-lllA) and Patna (zone-lllB). Mann-Kendall nonparametric test was employed fo r detection. of trends and the slopes of the trend lines were determined using the method of least sq uare linear fi tting. Since rice is the important crop in this part of the region, the vul­nerability of extreme rainfall indices on productivity also analysed using simple correlations. All the sites show an increasing trend of number of days with rainfall 10.0cm or more (very heavy precipitation event) during monsoon season. SaboLir shows a s ignificant increasing trend of 0.4 and 0.6 day/decade. respectively during monsoon and annually. During September, occurrence of heavy precipitation events over Madhepura recorded a significant positive trend of 0.4 day/decade. Highly significant magnitudes of increasing trends were noticed for Madhepura ( 46.6 mm/decade) and Sabour (27.5 mm/decade) for occurrence of highest five-day total precipitation during monsoon season. The results show statistically significant positive trends of number of days with rainfall> R2.5 cm for all the study sites during August. All the sites. the magnitude of highest 1-day and 5-days maximum rainfall is showing increasing trend. Increasing trends of fraction of rainfall due to extremely wet days is also showing increasing trend in all the sites. The rice productivity showed 10th degree polynomial technological trend in all the sites and steady increase in all the sites except Samastipur. The correlation between extreme rainfall indices dur­ing monsoon season a nd productivity anomaly index indicate that almost all the extreme rainfall indices contribute positive ly to rice productivity except P9SpTOT and R99pTOT over Sabour and R7.5 cm over Patna. lfwe assume the observed increasing trends in different extreme rainfall indices will continue, as estimated by the global circulation models, the chances of occurrence of intense rainfall events in near future will also increase proportionally and efforts should be made to prepare detailed site specific-at least at district level-disastrous management plan especially to reduce impact of extreme rainfall event on agricultural production system under changing climatic scenarios.

It> 2011 Elsevier B.V. All rights reserved.

Intergovernmental Panel on Clima te Change (J PCC, 2007) in its fo u rth assessment report (AR4) ind icated with very high confi­dence (90% probability of being correct) that hu man activities, since industrializat ion have caused the planet to warm by abo ut 1 •c

and future climate change is likely to affect agriculture, increase risk of hunger and water scarcity. Several works has been done fo r analyzing trends in monthly, seasonal o r annual rainfall for many areas of the globe using different sources of observed or grid­ded long-term data set (Peterson and Vose, 1997; Hansen et al.. 2001; New et al., 2001: Jones and Moberg, 2003; Subash and Ram Mohan, 2010a,b). However, these t rends can only address a sub­set of climate change issues. Therefore, changes/trends in extremes can be strong indicato rs of climate change as it has been hypoth­esized that in a warming world where the atmosphere can hold more water vapour and the hydrological cycle could become more active (Folr.inct et al., 2001 ). Moreover, extremes have more in flu -

• Corresponding author at: Project Directorate for Farming Systems Research (ICAR), Modipuram 250110, Mcerut, Uttar Pradesh. India. Tel.: +91 9457248070: fax: +91 121 2888546/2888571.

£-mail addresses: n.suby!IPrediffmail.com. n.suby@sify.com (N. Subash) . . 0378-3774/S - see front matter O 2011 Elsevier B.V. All rights reserved. doi: 10.1Ol6/j.agwat.2011.04.003

\

I 1374 N. Subash ec al./ Agricu/Cural Wacer Managemenc 98 (2011 ) 1373-1387

Table l Characteristics of agro-climatic zone. study area cand data period.

Agro-climatic zone Soil and topography Normal initiat ion/cessation Study site Data period Location of rainfall

Latitude Longitude Altitude (m)

Zone I (North west Heavy textured sandy loam to 12th June/30th September Samastipur 1960-2006 25.65"N 85.SO' E 52 alluvial plain) clay, medium acidic, nood to 10th October

prone, pH 6.5-8.4 Zone II (North west Light to medium textured 7th June/30th September Mad hepura 1974-2000 25.50 N 86.40-'E 42

alluvial plain) slightly acidic. sandy loam to to 10th October clay loam with saline/alkaline patch}s. pH 6.5-7.8)

Zone lllA (South Old alluvium sandy loam co 15th June/lOth September Sabour 1972-2003 25.14' N 87.40'E 37 alluvial plain) clay, slightly alkaline patches, co 10th October

pH 6.8-8.0 Zone Ill B (South Old alluvium sandy loam to 10th June/30th September Patna 1960-2007 2530' N 85.1S•E 52

alluvial plain) clay, slightly alkaline p.nches, to 10th October pH 6.8-8.0

ence on agricultural productivity as far as almost all the crops are concerned. Widespread increases in heavy precipitation events have been observed even in places w here total amounts have decreased (Bates et al., 2008). Future projections of climate change using Global and Regional Circulation Climate Models with differ­ent IPCC emission scenarios indicate an increase of about 5-10% in summer monsoon rainfall over India (NATCOM, 2004). It is also projected that number of rainy days may decrease by 20-30%, which would mean that t he intensity of rainfall is expected to increase. Extremes in rainfall also show increase in their frequency and intensity by the end of the year 2100. Based on observational and theoretical modeling studies lead to an overall conclusion that an increase in the frequency of heavy precipitation events is like to have occurred over most land areas over the late 20th century (Bates et al., 2008).

The joint World Meteorological Organization Commission fo r Climatology (CCl)/World Climate Research Programme (WCRP) project on Climate Variability and Predictability (CLIVAR) Expert Team on Climate Change Detection, Monitoring and Indices (ETC­CDMI) coordinated several efforts to enable global analysis of extremes (Manton et al., 2001; Peterson et al., 2001, 2002; Frich et al., 2002; Easterling et al., 2003; Griffiths et al., 2003; Mokssit. 2003; Aguilar et al., 2005; Peterson, 2005; Vincent et al., 2005; Zhang et al., 2005; Haylock et al., 2006; Klein Tank et al., 2006; Sensoy et al., 2006). Statistically significant increase in the occur­rence of heavy precipitation has been observed across different continents/regions/places by several workers (Klein Tank and Kiinnen, 2003; Kunkel et al., 2003; Groisman et al., 2004; Haylock and Goodess, 2004; Sen Roy and Balling, 2004; Alexander et al., 2006; Goswami et al., 2006; Guhathakurta and Rajeevan, 2007;

s

E:6] Zone I

CJ zone II

c:.3Sl Zone I I IA

LJzonelllB

Fig. 1.' Agro-ecological zones of Bi har and location of study sites.

N. Su bash er al./ Agricultural Warer Management 98 (2011) 1373-1387 1375

Table 2 Ext reme rainfall i ndices considered for the study.

SI.No. Descript ion/defini tion

Good precipitation days (precipitation :: 2.5 cm) Moderate precipitat ion days (::5.0cm) Heavy precipitation days (;:: 7.5 cm) Very heavy precipitation days (;:10.0cm) Very very heavy precipitation days(!:: 12.5 cm) Highest 1 day ~recipitation amount Highest 5 day precipitation amount

Acronyms Unit

NUm ber of days Number of days Number of days Number of days Number of days mm mm

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Moderate wet days (days with RR> 75th percentile of daily precipitation amount) Precipitation fraction due t o moderate wet days

R2.5cm RS.Ocm R7.5 cm RlO.Ocm R12.Scm RXlday RXSday R75p R7SpTOT R95p R9SpTaT R99p R99pTOT

Number o f days

% Very wet days (days with RR> 95th percentile) Precipi tation fract ion due to very wet days Extremely wet days (days with RR> 99th percentile) Precipitation fraction due to extremely wet days

Rajeevan et al., 2008). Rajeevan et al. (2008) used high resolution daily gridded rainfall data for India for 104 years and found that frequency of extreme rainfall events show significant inter-annual and inter-decadal variation in addition to a statistically signifi­cant long term trend of 6% per decade. The increasing trend of extreme rainfall events in the last five decades could be associated with the increasing trend of sea surface temperatures and sur­face latent heat flux over the tropical Indian Ocean. Another study using daily rainfall data for over SO years show significant increas­ing trend in extreme rainfall events over central India (Goswami et al., 2006). A recent study found that the intra-region variabil­ity for extreme monsoon seasonal precipitation is large over India and mostly exhibited a negative tendency leading to increasing frequency and magnitude of monsoon rainfall deficit and decreas­ing frequency and magnitude of monsoon rainfall excess (Pal and Al-Tabbaa. 2010). All the above studies, the authors used rainfall indices independently and not dealt with its impact on agriculture.

Table J Trends of rai nfall i ndices over different agro-climatological zones of Bihar.

Month/season and sites R2.Scm RS.O cm R7.Scm RlO.Ocm R12.S cm

June Samasripur Madhepura Sabour Patna

July Samastipur Madhepura Sabour Pama

August Samastipur Madhepura Sabour Patna

September Samastipur Madhepura Sabour Patna

Monsoon season Samastipur Madhepura Sabour Patna

Annual Samastipur Madhepura Sabour Patna

+ve -ve +ve +ve

+ve +ve +ve +ve

- ve +ve - ve +ve

- ve +ve - ve +ve

+ve +ve +ve +ve

+ve +ve +ve +ve

+ve -ve +ve +ve

+ve +ve +ve +ve

- ve +ve +ve +ve

+ve +ve +ve -ve

+ve +ve +ve +ve

+ve +ve +ve +ve

Shaded cells indicate significant at 95% level.

+ve -ve +ve +ve

+ve +ve +ve +ve

-ve +ve +ve +ve

-ve +ve +ve -ve

+ve +ve +ve +ve

+ve +ve +ve +ve

+ve - ve +ve +ve

- ve +ve +ve +ve

+ve +ve +ve +ve

- ve +ve +ve - ve

+ve +ve +ve +ve

- ve. +ve w e - ve

+ve +ve +ve +ve

-Ve +ve +ve +ve

+ve -ve - ve -Ve

-ve +ve +ve - ve

+ve +ve +ve -ve

- ve +ve +ve -Ve

Number of days % Number o f days %

To get the actual picture of va riability and trends in extreme pre­cipitation events on agriculture, it is better to restrict the study into micro level such as administrative district, basically to link it with the availability of crop data. Moreover, as far as occurrence of rain­fall.extremes with respect to agriculture is concerned, it is purely localized phenomena with its cause may be local/regional/global. but its impact will be localized. Hence, we have considered an administrative district as the boundary of the study area. Similarly, long data pertaining to rice productivity is available as administra­tive d istrict only.

Agriculture is the vital source of wealth in Bihar. 76% of its pop­ulation is engaged in agricultural pursuits directly or indirectly and 90 percent are marginal and small farmers. Principal food crops are rice, wheat, maize and pulses. Rice (Oryza sativa L) is a major food grain and it is cultivated during rainy season. South-west monsoon rainfall Uune to September) is the major source of water for rainfed agriculture. But its erratic distribution pattern-temporal as well

RXlday R7Sp R75pTar R95p R95pTar R99p

+ve - ve +ve +ve

+ve +ve +ve +ve

- Ve +ve +ve +ve

-ve +ve +ve - Ve

+ve +ve +ve +ve

-ve +ve +ve - ve

+ve - ve +ve +ve

+ve +ve +ve +ve

- ve +ve +ve +ve

- ve - ve - ve +ve

+ve +ve +ve +ve

+ve - ve +ve +ve

+ve +ve +ve +ve

+ve +ve +ve +ve

-ve +ve +ve +ve

-ve +ve -ve - ve

+ve +ve +ve +ve

+ve +ve +ve +ve

+ve -ve +ve +ve

+ve +ve +ve +ve

-Ve +ve +ve +ve

-ve +ve -ve +ve

+ve +ve +ve +ve

+ve +ve +ve +ve

+ve +ve +ve +ve

+ve +ve -ve +ve

-ve +ve - Ve -ve

+ve +ve +ve +ve

+ve - ve -ve • +ve +ve +ve +ve +ve

+ve -ve +ve +ve

+ve +ve +ve +ve

- Ve +ve +ve +ve

- ve +ve -ve +ve

- ve +ve +ve +ve

-ve +ve +ve +ve

R99pTaT

+ve +ve +ve +ve

+ve +ve +ve +Ve

-ve +ve +ve +ve

-ve +v~

+ve - ve

+ve +ve +ve +ve

- v& +ve +ve +ve

Tab

le 4

·Mag

nitu

de o

f tre

nds

of ra

infa

ll in

dice

s (p

er d

ecad

e) o

ver d

iffe

rent

agr

o-cJ

imar

olog

ical

zon

es o

f Bih

ar.

Mo

nth

/sea

son

and

sire

s R

l.5cm

RS

.Dem

R

7.5

cm

RlO

.Ocm

R

l 2.5

cm

R

Xld

ay

R75

p (d

ays

) R

75p

TO

T(%

) R

95p

(day

s)

R95p

TO

T(X

) R

99p

(day

s)

R99

pTO

T(%

)

June

S

amas

ripu

r N

N

N

N

N

N

N

N

N

N

0.

4 N

Mad

hepu

ra

N

N

N

N

N

N

N

N

N

N

N

N

Sab

our

N

N

N

N

N

16.3

N

N

N

N

N

N

Patn

a N

N

N

N

N

8.

7 N

4.

7 N

5.

J N

6.

6

July

Sa

mas

ripu

r N

N

N

N

N

N

N

N

N

N

N

N

Mad

hepu

ra

N

N

N

N

N

19.7

N

N

N

N

N

N

Sab

ou

r N

N

N

N

N

N

N

N

N

N

N

N

Patn

a N

N

N

N

N

N

N

1.

8 N

1.

8 N

1.

7

Aug

ust

Sam

asti

pur

N

N

N

N

N

N

N

N

N

N

N

N

Mad

hepu

ra

0.7

N

N

N

N

12.8

2.

2 13

.l

N

N

N

N

Sab

our

N

N

N

N

N

N

N

N

N

N

N

N

Pat

na

0.4

N

N

N

N

s.o

N

2.8

0.4

4.0

0.4

4 .1

Sep

tem

ber

.

Sam

asti

pu

r N

N

N

N

N

N

N

N

N

N

N

N

Mad

hepu

ra

0.8

0.7

N

0.4

0.3

3

3.7

N

2.

7 N

8.

6 N

8.

6

Sab

our

N

N

0.4

N

N

N

N

N

N

N

N

N

Pat

na

N

N

N

N

N

N

N

N

0.3

N

0.3

N

Mon

soon

sea

son

Sam

asti

pur

N

N

N

N

N

N

N

N

N

N

N

N

Mad

hepu

ra

N

1.3

N

N

0.4

28.9

N

N

N

N

N

N

Sab

our

N

l.O

0.9

0.4

N

9.6

N

N

N

N

l.4

N

Patn

a 1.

1 N

N

N

N

N

!.

l 1.

3 l.

3 2.

0 N

1.

6

Ann

ual

Sam

asti

pur

N

N

N

N

N

N

N

N

N

N

N

N

,Mad

hepu

ra

N

N

N

N

0.4

25.2

N

N

N

N

N

N

Sab

our

2.

1 1.

2 1.

0 0.

6 N

10

.4

3.2

N

2.5

o.s

1.7

3.3

Patn

a 0.

9 N

,

N

N

N

N

N

N

N

N

. N

N

N,

not s

igni

fica

nt.

--·--

--. -

---

---

----

-·· --

. -·

----

·~·-

---

.. ---

-· ---

----

---

---

-

'--.....

;<:

l:' "' a ;:,-

~ " ,...

' :.. ~

!)' ~ g ~ ~ ~ " ~ "' 3 "' :'! :;g ;;:;

~

,_

i:j " i' i:j co "

w .., O

'>

. .._

"'-....

:..

N. Su bash er al/ Agriculrural Wacer Management 98 (2011 ) 1373-1387 1377

I. Rainfa ll Index RX! day 350,-~~~~~~~~~~~~~~ 350..--~~~~~~~~~~~~~--.-

300

250

200

150

100

E 50

E o c

(a) Samastipur (Zone I)

- 1960 1970 1980 1990 2000 ~ 350 r----------------~ c

~ 300

250 .

200

150

100

50

(c) Sabour (Zone lllA)

0 .......... u..L:J\L'-U""-".14-'-UlLl.U\!Lll!.:l"-lll.l.lllLWl'-'.l,ll.llll.ILU,1.UJ.;lll.J.

1975 1980 1985 1990 1995 2000

Year

300 (b) Madhepura (Zo ne II)

250

200

150

100

50

0 1975 1980 1985 1990 1995 2000

350

300 (d) Patna (Zone 1118 )

250

200

150

100

50

0 1960 1970 1980 1990 2000

Year

2. Rainfall Index RXS day 600 ..,..-----------------.- 600 ~--------------~

E E .S

500 (a) Samastipur (Zone I)

400

300

200

100

0 1960 1970 1980 1990 2000

~ 600 r------------------. c "(ii 0::: 500

400 -

300

(c) Sabour (Zone lllA)

1975 1980 1985 1990 1995 2000

Year

500

400

300

200

100

0 1975

600

500

400

300

200

100

0 1960

(b) Madhepura (Zone II)

1980 1985 1990 1995 2000

1970

(d) Patna (Zone 1118)

1980 1990 Year

2000

Fig. 2. Time seri4!S of the extreme rainfall indices during monsoon season over different agro-climatological zones ofBihar(t. rainfall index RX I day; 2. rainfall index RX5days).

as spatial- affect the rice growth and thereby.inOuence production in one or other part of the i:egion every year. Any aberrations of rainfa ll pattern such as delay of monsoon, breaks in the monsoon activity, prolonged dry periods during the crop season and even continuous Oooding also affect the production. Tillering to Oower-

ing is the most critical stage when r'lce crop should not be subjected to any moisture stress. According to jearakongman et al. (1995). grain yields were severely reauced when standing water disap­peared more than 20 days before anthesis. while the presence of standing water 20 days after anthesis resulted in the highest yields.

-~u, ..... _._._..., ...... _. .. _...,.-.-lliiiioilliil_._.,

'/ 1

1373 N. Subash er al. / Agrirnlrural Warer Managemenr 98 (ZOl I) 1373-1387

Table 5 Trends and magnitude of rain Fall index (RX5day) d ur ing monsoon season.

Sire Trends Mag nitude

Samastipur(Zone I) +ve N Madhepura (Zone II ) +ve 46.6 Sabour ( Zone lllA) +ve 27.5 Pa ma (Zone 1118) +ve N

Shaded cells indicate significant at 9 5% level; N. nor significant.

Depending on climate, soils, cultivars and management practices, the total irrigation water requirement of the rice crop ranges from 450 mm in upland conditions to 1300 mm in lowland conditions (Yoshida, 1975, 1978; Doorenbos and Kassam, 1979). Even though monsoon enters over the main lands of India through l<erala Coast during the last week of May or first week of June, so that it can be • transplanted in puddled field after good initial monsoon showers. Similarly, the monsoon ~tarts its withdrawal from the northwest­ern part of the country from first September and it completely withdraws from the country by 15th October. Hence, as far as rice crop is concerned.June-September rainfall is considered as the core period. Even though Bihar falls under high rainfall region, recur­rent floods and droughts during the monsoon season is a serious concern. Surprisingly, the occurrence of severe drought/floods in recent years 2003, 2004, 2005, 2008 and 2009 in one or other agro­cl imatological zones, supports the IPCC (2007) fourth assessment report. The recent extreme rainfall deficit occurred over Bihar dur­ingJune and July months incurred a loss of 18.39 billion INR to state exchequer (Khan et al., 2009).

In the present study, the variability of extreme rainfall indices during monsoon months, season and annual for four stations viz .. Patna, Sabour. Samastipur and Madhepura districts, representing different agro-climatological zones ofBihar and its impact on rice productivity have been examined. We have also investigated the trends of extreme rainfall indices and estimated the slopes of the trend lines to know the magnitude of t rends.

2. Materials and methods

2.1. Study site

Biharis lying approximately between 21°58'1 O" and 27°31'1 S"N latitudes and 82 19'50" and 88"17' 40''E longitudes in the middle lndo-Gangetic Plains extending 483 km from west to east. Temper­ature varies from a maximum of 43 °C in summer to a minimum of around 5 "C in the winters. It receives medium to heavy rainfall during the monsoon season. This state embraces some of the most

Table6

Table7 Critical/cut-off e;:trem~ rainfall indices during monsoon season over different agro-climatological zones ofBihar.

SI.No. Rainfall indices Samastipur Madhepura Sabour Patna

1 R2.S cm (d ays) 13 13 6 12 2 R5.0 cm (d ays) 5 4 2 4.5

3 R7.5 cm (days) 2 2 <1 3.5 4 Rl O.Ocm (days) <1 <1

5 R12.5 c m (days) <1 <1 <1 <1 6 RXl day(mm) 120 125 80 125 7 RX5day(mm) 250 250 175 300 8 R75p (days) 25 27 25 24 9 R'5p (days) 20 21 17 20

10 R99p (days) 16 19 12 15 11 R75pTOT (%) 88 90 87 12 R95pTOT(%) 83 83 80 82 13 R99pTOT(%) 78 78 75 74

fertile lands oflndia, squeezed 'in between West Bengal.Jharkhand and Uttar Pradesh. reaches up to the Himalayas in the north and is completely land locked. Bihar is bounded on the north by Nepal, on the south by Jharkhand, on the east by West Bengal and on the west by Uttar Pradesh. The topography of Bihar can be easily described as a fertile alluvial plain occupying the Gangetic Valley. The plain extends from the foothills of the Himalayas in the north to a few miles south of the r iver Ganges as it flows through the State from the west to the east. The river Ganga flows right across it from west to east. North Bihar is ext remely fertile, the land being wa tered by the rivers Sarayu, Gandak and Ganga. The other rivers are the Sone. Poonpoon, Falgu, Karmanasa, Durgawati, Kosi. Ghaghara etc. Bihar is traditionally divided into (1) The North Ga nga plain and (2) The South Ganga plain. Rice (May/June-October/November) fo llowed by wheat (November/December-March/April) is the domi nant cropping sequence in this part of the study area. Based on soil characterization, rainfall, temperature and terrain, the state is divided into four agro-climatological zones w ith zone-I and zone-11 corresponding to north Bihar whereas zone-lllA and zone-lllB comprising districts of south Bihar. The soil. type and topogra­phy, normal climatic conditions of the agro-climatic zones are reported in Table 1. Four representative d istricts, viz., Samas­tipur (zone-I), Madhepura (zone-II), Sabour (zone-lllA) and Patna (zone-1118)- having more or less agro-climatological as well as agro-ecological conditions were selected based on data availability (Fig. 1).

2.2. Data

The daily meteorological parameters recorded at !ARI regional station, Pus a, Samastipur; Agricultural College. Sabour; Agricultural

F-Statistic a nd the Spearman rank corre lation coefficient (r,) between extreme rainfall indices and rice productivity index over different agro-cl imatological zones of Bihar.

SL No. Rainfall indices Samast ipur Madhepura Sabour Patna

F-Stat r, F-Stat r, F-Stat r, F-Stat r,

R2.5 1.963 032· 2.888 0.46- 0.014 0.02 3.210 0.25 R5.0 2.376 0.35' 1.753 0.37. 0.067 0.05 2.290 0.21

3 R7.5 0.673 0.20 3.383 0.48" 0.120 0.06 0.214 - 0.07 4 RlO.O 0.065 0.20 1.654 0.36. 0.042 0.04 0.000 0.00 5 R12.5 0.389 0.15 0.647 0.24 0.132 0.07 0.545 0. 11 6 RXl 2.737 0.37' 0.615 0.23 0.039 0.04 1.097 0.15 7 RXS 1.282 026 0.629 0.23 0.038 0.04 0.176 0.06 8 R75p 1.766 0.31" 3.433 0,49·· 1.967 0.25 7.417" 0.37.

9 R95p 0.643 0.19 3.586 o.so·· 0.194 0.08 9320·· 0.41' 10 R99p 0.960 0.23 2.248 0.41' 0.003 0.01 1.114·· 0.38' 11 R75pTOT 0.931 0.23 0.171 0.12 0.000 0.00 10.66" 0.43" 12 R95pTOT 0.297 0.13 0.530 0.21 0.782 -0.16 11.92" 0.45" 13 R99pTOT 1.179 0.25 0.538 0.22 0.994 -0.18 7.611'' 0.37.

· Significant at 95% level. .. Significant at 99% level.

.................. ..,,, ........ :;-::i..,.

•

N. Su bash et al./ Agricultural Water Management 98(2011) 1373- 1387

l. Rainfall Index R95p

30 (a) Samastipur (Zone I) 40 ~-------------~

(b) Madhepura (Zone II)

20

10.

"' 0 iii' 1960 1970 1980 a 40 .--- --------------..

1990 2000

(c) Sabour (Zone lllA) 30

20

0

1975 1980 1985 1990 1995 2000

Year

30

20

10

1975 1980 1985 1990 1995 2000

30 (d) Patna (Zone 1118 )

20

10

0 1960

1970 •1980 1990 2000 Year

100 2. Rainfall Index R.95pTOT

80 QJ Ol 2 60 c QJ

2 40 QJ

Q

20

0

1960 1970 1980 1990 2000 100

80 QJ -

'-

(c) Sabour (Zone II/A)

' ......... .._ .. ,..,.,. --·---,

O> <U n 11 llHlf· Ir

.. ·r--' n c 60 QJ

~ 40 Q

20

0

..

··~- n.

~I II

1975 1980 1985 1990 1995 2000

Year

. -

100

80 (b) Madhepura (Zone II

60

40

20

0 1975 1980 1985 1990 1995 2000 100

80

60

40

20

0 1960 1970 1980 1990 2000

Year

1379

r I

I I

1•

1380 N. Su bash ec al. / Agriculcural Wacer Manng•menc 98 (2011) 1373- 1387

3000

2500

2000

Samastipur

Linear - R2=0.00 (D-W Statistic:1.43) 10th Poly- R2=0.94 (0-W Statistic:3.01)

~ 1500 1 Ii

~1=: r- ----- --~ 0 ~~~~~·~~~~~~~~~~-.-~~~-1. 0

3500 ~~~~~~~~~~~~~~

3000

' 2500 i 2000 1

1500

Madhepura

Linear - R2=0.40 (0-W Statistic:1.67) 10th Poly- R2=0.74 (0-W Statistic:3.07)

• • -.

i

I

- - ,1,i -- Tenth order polynomial fit

• --- linear fit • Productivity (kgiha) J

-5 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010

l:::! ~ 3500 : 0 • 1 i2 3000 ·l

2500 j i

2000 J

1500 ~ •

500 J

Sabour Linear - R2=0.14 (D-W Statistic:2.07) 10th Poly- R2=0.27 (D-W Statistic:2.46) •

•

• • •

• •• •

• • • . ..

•

0 ~' ~~·~...-~~~~~~-.-~~~.--~~-1

3500 l

3000 j 2500 i 2000 1 1500 j

1000

• . . •• • • • l

• l Linear - R2=0.31 (D-W Statistic:1.26) i

• 10th Poly- R2=0.65 (D-W Slatistic:2.45) I 1960 1970 1980 1990

Year 2000 2010 1970 1980 1990 2000 2010

Year

Fig. 4. Rice productivity trends ( linear and tenth order polynomial fit) and its Ducbin--Wateson (OW) test statistic for different agro-climatological zones of Bihar.

Research Institute, Patna and RARS. Rajendra Agricultural Univer­sity, Madhepura were collected. These observatories have been installed long back under the guidance of technically qualified offi­cers and calibrated the instruments according to the IMD/WMO standard. The productivity of rainy season (kharif) rice over rhe selected sites from 1960 to 2008 was taken from the Directorate of Rice, Ministry of Agriculture. Govt. of India and is available on-line at http://wwvy.dacnet.nic.in.

2.3. Rainfall indices

A list of over SO internationally agreed climate change indices for temperature and rainfall (WMO-CCl/CLIVAR) are available on web site (http://www.wmo.int) with their explanation and equations for calculating them. For the present study, we have considered a set of 13 rainfall indices (Table 2 ). From the list pre­scribed by WMO. we have not considered indices of RlO mm and R20 mm. because rainfall events of 1 O or 20 mm are quite com­mon in the Indian sub-continent during the southwest monsoon season Uoshi and Rajeevan. 2006). Hence, we have considered R2.Scm. RS.Ocm, R7.Scm, RlO.Ocm and R12.Scm to study the trends in different precipitation frequencies of rainfall greater than or equal to 2cm. Scm, 7cm, 10cm and 12.Scm. The defi­nitions of indices allow seasonal and monthly partitions and we have considered month-wise analysis during the southwest mon­soon season Uune-September). the main rainy season in this part. Most of the indices are defined in terms of counts of days cross­ing the thresholds either absolute (fixed) thresholds or percentile (variable) thresholds. Annual/seasonal/monthly day-count indices based on percentile thresholds are expressions of anomalies rela­tive to the local climate. Extreme rainfall indices considered for the

analysis are calculated for each year for each station. Since most of the indices are counts of days, a non-parametric model will be the best sui ted to test the significance rather than using Srudents' t test Uoshi and Rajeevan, 2006). Hence. we have used the Mann-Kendall test to examine the significance of the trend.

2.4. Mann-Kendall test

For all above indices. the Mann-Kendall nonparametric test. as described by Sneyers (1990). was applied in order to detect trends. The Mann-Kendall test has been used by several researchers to detect trends in hydro-meteorological time series data (Serrano et al.. 1999: Brunetti et al., 2000a.b: Subash and Ram Mohan. 201 Oa.b ). The slopes of the trends were calculated by fitting the data series into method ofleast-square linear fitting. Mann-Kendall test basically involves the ranks obtained by each data in the data series. Then t ime series values (X1, X2, X3, . ..• Xn) are replaced by their relative ranks (Rl, R2, R3, ...• Rn) (starting at 1 for the lowest up to n) (Kundzewicz and Robson. 2000; Chiew and Sirivardena. 2005).

The test statistic Sis:

where

sgn(x) = 1 fo r ·x > 0

sgn(x ) = O for x = O

sgn(x) = -1 for " < 0

N. Subash et al. / Agricultural Water Management 98 (2011) 1373-1387

.. >J

.. ~ i "· t

~--~~ ... -:-- . j" . ... '

f ~ ~~ A

t:(1re<!"91( \'4: ~t-"{1ik>>

. . ---~

i==t:J <IC NI "° .'C l¢ :Ill

Pr.0~1r:k:~'Ol'l4,~ l() .. t:1tl'l"Wf "'"•f ~~<R~ron

'¢ • -= 1 " ·. .,, = ·~1· .... :--- ' e- l··· ~- - • ·1! • •

IQ • '4:!

"" . . ,,.. 1 ~ '

'='!t * {I~ !'<' "' I-"! 1') f' 7~ ,-, «o ., ii; ~t i=itcc.;a:.')(ic:., frar-.:wit1 ::tUt :3 r."ICM :.v.- Pf!K'~OO'I tr.c~.:iu• ~ ~ '4-olt~l)'.i fR75:>TOT; . iwoet:&l:;sfRMpTOT)

(a) Samastipur

· : ... "

... 1·:_: - .;_· ·i. i .;i1' : ;

-<l j • ..w. • I • l

~=~ I=~ _______ : " ' • ""

12 : • ,_ ~ ~ o. , 4 4 a te o : • Jt • ts

GOOdpreq,itai'O'td."l\'S ;Al. :c:mJ ~~~~Uj1 1A$.WnJ ~=-~de,..:R"! 5oltf .. , ., __ ------- ...

.. :==-'-<~}'"" "' . ' • · . . . ' .. .

,..,~ Ml ;, ~l 3":

ProcP!fONicn fl'K:ot ;w lo •.r:re~ W9!.d'fS IRGfpfOT1 •

(c) Sabour

I l > • ~ !O . ~~EIOM<Qys

~ •O i

LL:l~:J __ ·_ <:" I 2 J _..,'-'II; l~~t•l~ IC01Q)1."'Q Vtf'IW')'Mao.yptt-:.'Pb.,,,,OCr)s HIQesc 10llypi'~~

... ~R"l2_~) _ _ _ l!G(RXIO"Yl , j"

f ~--- . J. ·=-· " i~~----= _l -- ~ f e lea !'!oil MG ~ 2Ei lSt! -CD I• II Ji0 :': :.. M 4.f .liO l: .i. 1• ' ' ~ ,"fJ l l ~· it C. tfligest 5 f1:Z/ ;ite¢pla(lc" Ol'nCul:: Uodtt* wt"!d.eo,'S ift~.SOJ •J_,wtt days tH"59! lRX!OJ)

11:1 ---------- -... . ""

... .. I =-== : l ·~ '' •"-'•n ~M IOl$9i) ~~ ~~MOO.IOl'l'IOIMtr.t.s ~tlon 'ract1»ouotio ,.r-,

~ 1i i• ~ '' ~ n M ~

t._ ---~-1__ -· .: 1$ 1'0 '~ to .u )Q it

~:r~!i(:n~~~ 'll!'et4#fs~pTOI')

'lf!Ol dO'(I 1R1'pTOT) ....: da71; CR~P"TOn

(b) Madhepura

(d) Pa tna

Fig. 5. Rela tion between productivity anomaly index and different rainfall indices over different study districts (a-d).

1381

I I

' l/ I

I I

I 1382 N. Subash ec al./ Agricultural Wacer Managemenc 98 (2011 ) 1373- 1387

(a) Samastipur

~ mmw01~10mm~0mm~m0m0mm - mmrummmmmmmmmmmmm~~m R;.Jem Qi] LJU W 0 CJ I AS I CJW JJIAS LJU ~ WIJ,11S! Qi§] W [ill LJU 0 Qi§]

~ mmmoooom~~mmommmoom R~ omooooom~moooommoom RXldAy 0000m0wQ00mw0000www ~.mmwmmmmmmmmmmmmmmmm ~ m00mm~rummmmwmmwmwmw ~ mmmmmmmm~mmmmrnmmmmm ~ mmmmmmmmmmmmmmmmmmm ~ mm0mmmrum00mwm0mmm0m R9~pTOT LJJ m w w w w m GJ w LJJ GJ LJJw w 0 w w m w ~ mwmmmmmmwmmmwmmmwmm PAI DBCSillD&m1rn!BEB~E:J&mDl'iiliimI•o&mmo

R'-'lcm

' R79'cm

Rio.Gent

RX~ay

~p

R<;?p

rt7~pTOT

AWpTOT

P;>I

S3

(b) Madhepura 911 gr H

Year 0

[ill GJ GI] G!] JJIAS IJr;.sJ ~ W [Ji] [ill W !JtAsl W LJU LJ] I J1s I GiJ JJtAsi OJ IT) GiJ GJ Qi] LJU WI Jrs I

wowoowwmwwwE§Jw moooo1J1As1mmmmmmm ooooom00ooomQ"Dm 00w0ww000m000 www000w0m0mmm www w ~ m1JIA1Cillw0 w ww mm00mmmlliJmmm~Q[J rnmmmrn1J1.\S1mCillmmm~m 00CD0ITJCD0000w0ITJ wwwwwwmwmwwmw wwwwwmwwwwwmw Dfl~jiijEJD~G@Mllffi~NmDD~D

~ ~ ~ ~ ~ W M ~ ~ ~ ~ n Year

3

Fig. 6. Variability oFrainFall extreme indices over (a) Samastipur, (b) Madhepura.

If the null hypoth~sis H0 (i.e., there is no trend in the data set) is true, thens is approximately normally distributed with:

The magnitude of the significant trends per year (slope) was computed by fitting the time series into linear fitting and converted the same into decadal magnitude by multiplying by 10 to compare the trend difference between agro-climatological zones. µ. = 0

a= n(n-1X2n+S) 18

The z-statistic is therefore (critical test statistic values for var­ious significance levels can be obtained from normal probability tables):

15 1 Z= ­aO.S

A positive value of S indicates that there is an increasing trend and vice versa.

2.5. Rice productivi ty

The production of rice depends on type of soil, seeds used, crop area, availability of irrigation facilities, fertilizers, pesticides and also on the government incentives to the farming sector dur­ing a year as well as on the meteorological parameters such as rainfall, temperature, relative humidity and solar energy. The non­meteorological parameters, i.e., the total technological inputs to the farming sector have been growing steadily and are difficult to quantify. Therefore, to know the pattern of trends and to quantify the growth rate of total technological inputs to the agricultural sec­tor the actual productivity y.ras fitted best lit model using "Curvefit"

R1~eN

A~~

"""' A~

!17!•101

A99pTOT ...

ll"-'cm

FtJ.O~m

n:.oe"' F\10.0oer1:t

A't:-'c.m

flJCldAy

A~

R!Jp

MJp

A!l9p

fl7~pTOT

Fl~pTaJ

~TOT

PAI

N. Subash er al./ Agricultural Water Management 98 (2011) 1373-1387

(c) Sabour

l mmmm0mmm00mmm00m0mm0mm0m~ m~~~mwom~mmmmmmmmm~mm~mmm mm~oooom0ooommmmmmmmm~~~m ornooooooooooommmmooomomo~o ommooooooooommomooomooo00 mmmm0mm00m0mmrnmmmmwmrnm~mm mmmmmmmmmmmmmmmmmmmmmmmmm ~mm~mmmmmmmmmm~m~mmmmmmmm ~~mmm~mm~mmm~mmm~mmm~~m~m ~~mmmmmm~mmm~mmmmmmm~mmmm mmmmmmmmmmmmmmmmmmmmmmmmm mmmmmmmmmmmmmmmmmmmmmmmmm mmmmmmmmmmmmmmmmmmmmmmmmm D~~~DDD•D~E~DDD~DDD~~oeoo

1T::3:'9.SOi1

Year

00wwwww LlLlw~ww~GJ wwGJwwGBJGJ mm Q!filwm o CEJ 00mmoom wGDGDCTICTI D!JITJ [ZJU!JGJGJGJGJGJ @~q Neva:P:e ?Al D Zero •r.:i!t:e

wf2J wGJGi!J Cill ~ Qj]QJ Q]ITJITJ@J~ rnmmmmm~ 0000m00 ·GJG!JG!JGJG.JwCTI LJJ[i!JQ!J[§JGJ [§JG]

CJ~ G3 r:::::B • G:J E2J

Fig. 7. Variability of rainfall extreme indices over(c) Sabour.

1383

program. Several studies were reported to fit the productivity into linear/exponential/quadratic equations to define ~he technological trend (Gopinathan, 2000; Kandiannan et al., 2002; Challinor et al., 2003, 2005; Ortiz et al., 2008; Subash et al.. 2009). To normalize the rice productivity, the actual productivity has been converted in to indices. The productivity anomaly index (PAI) was taken as the percentage of the technologica l trend productivity (best fit) to the actual productivity. The PAI fo r the ith year is

m onsoon as well as annual season (Table 3). Sabour has s hown a s ignificant positive trend in annual number of good precipita­tion days (2.1 day/decade) wit!} 2.5 cm or more rainfall. Similarly, Patna d istrict also recorded a significant increasing trend of 1.1 and

PA/.== (P; - TPi) x 100 I TP;

where PAI; is the rice productivity anomaly index for the ith year, P; is the actua l productivity fo r the ithyear and TP; is the technological trend productivity for the ith year. The influence of extreme rain­fall indices with rice productivity anomaly was determined using simple correlations.

3. Results and discussion

3. 1. Trends in rainfall indices

3.1.1. R2.5 cm-number of days with rainfall 2.5 cm or more The . signi ficant positive trends of 0.7 ·d ay/decade and 0.4

day/decade have been obseived for Madhepura and Pat na. respec­tively fo r August. All the sites, positive trends were obseived during

0.9 day/decade during monsoon and annual . respectively (Table 4). As far as agriculture is concerned, · the monsoon season, positive trend ofnumbel\i¥~~~1llit.li+!· 2.5 cm or more will be highly beneficial.

3.1.2. RS.a cm- number of days with rain/alt 5. All the sites, positive trends were obseive,..,:;;:;;;n;:;.c,;;:;inu:;~

well as annual season. Sabour has shown a sign~~mrnR?im~1 (1.2 day/decade) in annual number ofmodera~llt.E~~~~iltl with 5.0 cm or more rainfall. But during monsCt2'~a~~~?l cant increasing trend of 1.3 and 1.0 day/decadit§~~~~~~ been obseived for Madhepura and Sabour. T of number of moderate precipitation days d .b;:,;,;:.:,:::-:r.::==::-:i son may be due to the contribution of its incr·~m;;;:n:ml:l:71trmih September. Hence, the adoption of increasing W:.~i*l=~idit:l~ help to store the water within the field, so that t~!ii.~:!¥~~pf riences water stress conditions during the brea

3.1.3. R7.5 cm~number of days with rainfall 7.5 cm or more For m onsoon and annual season. all the s ites show positive

trend. Moreover. Sabour shows significant increasing trend of 0.9

I .

I

N. Su bash ec al. / Agriculcural Wacer Managemenc 98 (201 1) 1373-1387 1385

3.1.7. R75p and R75pTOT All the sites have shown pos1t1ve trends during monsoon

(Fig. 3). Patna has recorded a significant increasing trend of 1.1 day/decade. As far as individual months are concerned, no deft-

• nite pattern have been observed. However. Madhepura shows a significant increasing trend of 2.2 days/decade during August. A significant increasing trend of 3.2 days/decade has been observed for Sabour annually. The index R75pTOT determines the contcibu­tion of moderate wet days (rainfall with more than 75 percentile) on the total raint"all of time scale considered. All the stations, the contribution from the rainfall amount higher than 75 percentile has increased during monsoon as well as annually. However, a sig­nificant increasing trend of 1.3 percent/decade has been observed for Patna during monsoon season. Similarly, significant positive trend were observed during June (4.7 percent/decade), July (1.8 percent/decade) and August (2.8 percent/decade) for Patna. Sim­ilarly, two more indices fo r 95 percentile and 99 percentile are also calculated. The R95p index also shows similar pattern dur­ing monsoon and annual. However, significant positive trends have been noticed during August (0.4 day/decade) and September (0.3 day/decade) for Patna.

3.2. Rice productivity trends

The rice productivity values were fitted into different mod­els in curve fit program and found that R2 value is higher for 10th degree polynomial fit for all the districts (Fig. 4). The rice crop continuous to show steady increase in all the Districts except Samastipur for which the growth is almost nil. Even though, there is a best fit for 10th degree polynomial curve, the scattering of points from the trend line indicate that the year-to-year variabil­ity is more in all the districts. In Patna, the productivity decreased tremendously from 2004 onwards and not recovered. The higher productivity during 2008 at Samastipur may be due to even dis­tribution of rainfall pattern, particularly during August-September months. Samastipur falls under the category of very low productiv­ity ( <1000 kg/ha) group during the triennium ending 2007 while all the other districts faH under the category of low productivity group ( <1500 kg/ha).

3.3. Relation between rice productivity and rainfall indices

The influence of extreme rain fall indices on rice productivity over different districts is shown in Fig. 5. There is a positive correla­tion exists between all the extreme rainfall indices and productivity anomaly indices over Zones I and II. The Spearman rank correla­tion coefficient (rs) between R2.5cm, RS.Deni, RX1day and R75p with productivity anomaly index show significant correlations in zone I (Table 6). However in zone II. between R2.5 cm. R5.0 cm, R7.5 cm, R10.0 cm, R75p. R95p and R99p and productivity anomaly indices show significant correlations. The critica l value/cut-off of extreme rainfall indices, which is the value of intersection point of the trend line plot between extreme rainfall indices and productiv­ity anomaly indices on the extreme rainfall indices axis (Table 7), indicated that the number of good precipitation days (R2.5cm)>13 days during the monsoon season Uune-September) increases the rice productivity in zones I and II. Interestingly in zone I. the pos­itive correlations between highest 1-day precipitation amount to rice productivity shows a rainfall amount of 120 mm or more may be advantage for the rice crop. Out of,8 years received more than 120 mm rainfall in 1 day during monsoon season, 4 years produced higher rice productivity. The occurrence of 1 day maximum dur­ing watet sensitive phonological phases is important rather than the entire season; however, rice crop requires water in almost all the growth stages. The temporal variability of occurrence of 1 day maximum rainfall for these 8 years indicate that three years (1996,

1997 and 2003) RX1day occurred in June, 2 years (1989 and 1990) in July, 2 years (1995 and 2002) in August and one year (2006) in September (Fig. 6). Thus, there is no clear indication with the dis­tribution of occurrence of maximum 1 day rainfall with PAL But maximum number of 1 day maximum rainfall occurred in August followed by July, which coincides with the vegetative/maximum tillering phase. As far as occurrence of RXSdays rainfall greater • than 250 mm rainfall occurred in 7 years (1989, 1995, 1996, 1997, 1999, 2002 and 2006).' Even though there is a positive correla­tion between RX5day and productivity anomaly index, out of these 7 years (for which RX5days > 250 mm), 4 years got negative pro­ductivity anomaly index. Thus, as far as Samastipur district is concerned, occurrence of more than 250 mm in 5 days may be detri­mental to rice productivity, particularly during August However. in zone II, out of 7 years received 13 days or more R2.5 cm, 5 years got positive productivity anomaly index. Interestingly, the positive correlations between highest 1 day precipitation amount to rice productivity shows a rainfall amount of 125 mm or more may be advantage for the rice crop is concerned. Out of 7 years of more than 125 mm rainfall in 1 day during monsoon season, 4 years produced higher rice productivity. Even though there is positive correlation between RX5days with productivity anomaly indices, out of 5 years (1994, 1995, 1997, 1999 and 2000) received more than 250 mm cumulative rainfall in 5 days during monsoon sea­son, 3 years ( 1997, 1999 and 2000) produced positive productivity anomaly index explains the extreme rainfall may have provided beneficial for rice crop. However, in 1999 and 1995, even wit/1 higher RXSdays responded negatively to productivity advocated that the occurrence of time of extreme event with the phonolog­ical stage of the rice is important rather than during the season (Fig. 6).

As far as zone llJA is concerned, out of 13 negative PAI years, maximum highest RXl day occurred during August (6 years-1974, 1975, 1979, 1982, 1987 and 1994) and minimum during Septem­ber (3 years-1983, 1991 and 2001 ). Interestingly, highest RXl day maximum rainfall occurred during June in 3 years (1984, 1993 and 2003) and these years the productivity falls in the positive side (Fig. 7). Out of 17 years received more than 175 mm rainfall in

·RX5day, 10 years got positive productivity anomaly. Interestingly, 4 out of 7 negative productivity anomaly index years received a rainfall greater·than 175 mm in July. This indicated that extreme rainfall during July may have delayed the transplanting operations or damaged the just transplanted crop and thereby decrease the productivity considerably. The negative correlation of R95pTOT and R99pTOTwith rice productivity anomaly pointed towards the vulnerability of rice crop to irregular distribution pattern as this represents the fraction of rainfall received during the very wet days and extremely wet days. As far as zone IIIB is concerned. out of21 negative PAI years. 9 years (1962, 1969, 1973, 1982, 1983, 1987, 1991, 1992 and 2007) received maximum RX5da.ys rain­fall in August (Fig. 8). Out of 4 years having more than 4 days of R7.5cm (1987, 1995, 1Q97 and 1999), only one year (1987) got negative rice productivity anomaly. In 1987, 8 days received R7.5 cm and even 6 days and 4 days, respectively received RlO.O cm and R12.5cm rainfall. This may be the reason for negative pro­ductivity anomaly. Out of 7 years (1967, 1981, 1990, 1997, 2002, 2006 and 2007), 3 years (1981, 1997 and 2007) occurred negative productivity anomaly, interestingly all these negative productivity years the value of RX5days reached more than 425 mm. How­ever, these extreme rainfalls occurred in different months in these years. Since the critical duration of most of the water sensitive phonological phases of rice crop will be between 10 and 15 days and therefore to get more clear understanding of vulnerability of extreme rainfall indices on rice productivity, it is better to analyse the rainfall data at biweekly time interval rather than monthly or seasonal.

' ' •

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I 1384 N. Su bash et al. / Agricultural Water Management 98 (2011) 1373-1387

(d) Patna

~ ~~~~mm~mmmmmmmmmmmrnmmrnmmm - ~o~m~m~mmmm~mmm~~m~mmmm~m ~ moomm~mmo~mmm~mmmommmmmom ~ moomomomo~mmommmoommmrnooo ~ moomomomomoooommoo~oomooo ~~ mmmmmm~mmmmm~mmmmmmmmmmmm ~ mmmmmrnmmmmmmmmmmmrnmmmmmmm ~ ~mmmmmmm~mmmmmmmmmmmmrnmmm -p mmm~mmm~m~~mmmmmmm~mmmoom~ -p m~m~mmm~mm~mmmmmmm~mmrn~m~ ~ · mmmmmmmmmmmmmmmmmmmmmrnmmm ~ mmmmmmmmmmmmmmm m mmmmmmmmm -~ mmmmmmmmmmmmmmmmmmmmmrnmmm PAI oe moo w l!DD miE m1 ooE eo e!!!D o e oe e ~ o

A1.0 .. 0crn

~oy

R7~p

R9'lp

R99p

R7'pTOT

A95pTOT

R99pTOT

~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ro n ~ n n n ~ n m n ~ ~ ~ ~ ~

Year mm~mrnm~m0m~mrnmmmmrnmm~~~m m~~mmrnmm~mm~m~~moorn~~m@mm m~m~o~momrnm~mmm~~rnomo~m~ o~mmo~momommmmomomooo~mm om~DD~DDDDDD~mDDDDDDD~mo mmmmmmmmmrnmmmmmmmmrnmmmmm mmmmrnmmmmmmmmmmmmmm mrnmmm mmmmmmmmmmm~mrnmmmm~mmmmm mmmmmrnmmmmmmmrn~m~m~m~mmm 00m000mmmmmmmm~m~~wm~mmrn mmm00mmmrnmmmrnmmmm0mm0mmm mmmmmmmmmmmmmmmmmrnmmmmmm 0mm0mmmmmmmmmm0mmmmmmmmm oo~ooo~~oowoeoom~ooeooao ~ ~ ~ " ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0

Year o~PAJ D Zeroor.ilue

Fig. 8. Variability of rainfall ex[reme indices over (d ) Patna.

and 1.0 day/decade during monsoon and annual, respectively. As far as monthly distribution is concerned, Sabour shows a significant increasing trend of 0.4 day/decade during September. The increas­ing trend of heavy precipitation days with precipitation 7.5 cm or more during monsoon season as well as annual draw attention to the importance of disaster preparedness to minimise the vulnera- . bility.

showed positive t rends and Samastipur and Patna showed negative trends. Even though, Madhepura showed a significant increasing of 0.4 day/decade during monsoon and annual season.

3.1.6. RXIday and RX5day All the sites show increasing t~end of highest 1-day maximum

rainfall during monsoon season (Fig. 2). However, a decreas­ing trend of RX1day has been observed fo r Patna annually. Madhepura and Sabour show an increasing trend of 28.9 and 9.6 mm/decade, respectively during monsoon season. But indi­vidual months. Madhepura shows significant increasing trends during July (19.7 mm/decade), August (12.8 mm/decade) and September (33.7 mm/decade). Highly significant magnitudes of trends were noticed for Madhepura ( 46.6 mm/decade) and Sabour (27.5 mm/decade) fo r occurrence of highest five-day total precip­itation (Table 5). The higher magnitudes of trends in highest 1-day maximum and highest 5-day total point towards the significance of taking up offload tolerant and tall rice varieties in this part of the region. It also suggests the importance of construction of drainage structures/facilities to drain out excess water immediately from the agric~ltural fields.

3.1.4. Rl 0.0 cm- number of days with rainfall 10.0 cm or more All the sites show increasing trend during monsoon sea­

son. Sabour shows a significant increasing trend of 0.4 and 0.6 day/decade, respectively during monsoon and annual season. Dur­ing September occurrence of heavy precipitation events over Madhepura recorded a significant positive trend of0.4 day/decade.

3.1.5. R12.5cm- number of days with rainfall 12.5 cm or more Monthly distribution during monsoon season shows that all the

sites show an increasing trend during June. But no definite pattern has been observed during other months. However, as far as mon­soon season is concerned. all the sites except Patna show positive trend. When we considered annual rainfall. Madhepura and Sabour

1336 N. Subash er al. / Agriculwral Water Management 98 (2011 ) 1373-1387

4.. Conclusions

Analysis of variability and trends of extreme rainfall indices pro­vide an opportunity to evaluate the capability of present disaster management/contingency plan and mechanism to cope up with the situations under possible/projected climate change scenar­ios/conditions. We have examined the trends in 13 extreme rainfall indices over 4 stations representing different agro-climatological zones of Bihar. India, which is part of the middle lndo-Gangetic plains. contribute largely to food security of the region. The study also tried to find out the vulnerability of rice productivity. which is the main staple food grain of the region, to extreme rainfall indices, by the way of simple correlations. The results show statistically sig­nificant positive trends of number of days with rainfaJl > R2.5 cm for all the study sites during August and this may be beneficial to rice crop, as this period may coincides with the grain filling/maximum vegetative pheno phase of the crop. All the sites, the magnitude of highest 1-day and 5-days maximum rainfall is showing increasing trend and there is urgent need to take up flood tolerant and tall rice varieties in this part of.the region and measures should also be taken to drain out excess water from the agricultural plots imme­diately. Increasing trends of fraction of rainfall due to extremely wet days suggest the need for creation of water storage structures and increase of bund height/or creation of bunds in rice fields, so that rain water can be stored/harvested and utilized as and when required for the plants and also to increase the ground water table.

The rice productivity showed 10th degree polynomial techno­logical trend in all the sites and steady increase in all the sites except Samastipur. The correlation between extreme rainfall indices dur­ing monsoon season and productivity anomaly index indicate that almost all the extreme rainfall indices contribute positively to rice productivity except P95pTOT and R99pTOT over Sabour and R7.5 cm over Patna. However, based on F-statistic, no significant correlation existed between extreme rainfall indices and rice pro­ductivity anomaly over Samastipur. Madhepura and Sabour. Highly significant correlations were noticed between R75p, R95p, R99p, R75pTOT, R95pTOT and R99pTOT over Patna. The lower value of Spearman rank correlation coefficient and scattering of points over the trend line of plot between rice productivity anomaly index and extreme rainfall indices indicates the higher year-to­year variability and limitation of use of these regression equations for prediction/forecasting of rice productivity. The critical/cut-off value of extreme rainfall indices and its relation to rice productiv­ity anomaly indices for.study sites provide a base for vulnerability assessment and also help to prepare contingency/management plan under different scenarios under possible climate change con­ditions.Since the duration of different water sensitive pheno phases of rice crop vary between 10 and 15 days, biweekly analysis of extreme rainfall indices may provide more accurate understanding. If we assume the observed increasing trends in different extreme rainfall indices will continue. as estimated by the global circulation models, the chances of occurrence of intense rainfall events in near future will also increase proportionally and efforts should be made to prepare detailed site specific-at leas\ at district level- disastrous management plan especially to reduce impact of extreme rainfall event on agricultural production system under changing climatic scenarios.

Acknowledgements

Authors are thankful to National Agricultural Innovation Project (NAIP) for providing necessary funds to taken up this study. The authors are also thankful to joint Director, Agricultural Research Institute. Rajendra Agricultural University; Project Coordinator, AICRPAM, CRIDA. Hyderabad; Head, !ARI Regional Station, Pusa and

RARS. Madhepura, Rajendra Agricultural Universi ty for providing the meteorological data. We are thankful to the two anonymous reviewers who have made critical comments and suggestions to improve the manuscript.

References

Aguilar. E., Peterson. T.C .. Ram1irez Obando, P .. Frutos, R.. Retana, J.A .. Solera, M .. Soley,) .. GonzaTlez GarciTa, I .. Araujo, R.M .. Rosa Santos. A . Valle. V.E .. Brunet. M .. Aguilar, L. Alvarez, L. Bautista, M., Castanon, C .. Herrera. L. Ruano, E .. Si nay, J.J .. Sanchez, E., Hernandez Oviedo, C.I .. Obed. F .. Salgado. E .. Vazquez, j.L. Baca, M .. Gutierrez. M .. Centella, C., Espinosa,) .. Martinez. D .. Olmedo, B .. Ojeda Espinoza. C.E .. Nunez. R., Haylock, M .. Benavides, H .. Mayorga, R .. 2005. Changes in pre­cipitation and temperature extremes in Central America and northern South America 1961-2003.J. Geophys. Res. 110, D23107, doi:10.1029/2005JD006119.

Alexander, LV .. Z"ang, x .. Peterson. T.C.. Caesar. J .. Gleason, B .. Klein Tank. A.M.G .. Haylock. M .. Collins. D .. Trewin, B .. Rahimzadeh. F .. Tagipour. A. Rupa l<umar. K.. Revadeku. J .. Griffiths, G., Vincent, L, Stephenson. D.B .. Burn. J .. Aguilar. E., Brunet. M .. Taylor, M., New, M .. Zhai, P .. Rusticucci, M., Vazquez-Aguirre, J.L. 2006. Global observed changes in daily climate extremes of temperature and precipitation.]. Geophys. Res. 11 1, D05109, doi:10.1029/200SJD006290.

Bates, B.C., Kundzewicz, Z.W .. Wu. S .. Palutikof,j.P.(Eds.), 2008. Climate Change and Water. Technical Paper orthe Intergovernmental Panel on Climate Change. IPCC Secretariat, Geneva, p. 210.

Brunetti, M .. Buffoni. L. Maugeri. M .. Nanni, T .. 2000a. Precipitation intensity t rends in northern Italy. Jntj. Climatol. 20, 1017-1031.

Brunetti, M .. Maugeri, M .. Nanni. T .. 2000b. Variations of tem~rature and precipi­tation in Italy from 1866 to 1995. Theor. Appl. Oimatol. 65. 165-174.

Challinor. A.J., Slingo,j.M .. Wheeler. T.R .. Craufurd, P.Q.. Grimes, D.1.F .. 2003. Toward a combined seasonal weather and crop productivity forecasting system: deter­mination of the working spatial scale. J. Appl. Meteorol. 42. 175-192.

Challinor, A.J .. Wheeler. T.R .. Slingo, J.M .. Craufurd. P.Q. Grimes. 0.1.F .. 2005. Sim­ulation of crop yields using ERA-40: Limits to skill and non stationarity in weather-yield relationships.). Appl. Meteorol. 44, 516-531. .

Chiew, F .. Sirivardena, L, 2005. TREND: t rend/change detection software manual. CRC for Catchment Hydrology 29. www.toolkitnet.au/t rend.

Doorenbos.J .. Kassam, A.H .. 1979. Productivity response of water. FAO Irrigation and Drainage Paper 33.

Easterling. D.R.. Alexander. LV .. Mokssit, A, Detemmerrnan, V .. 2003. CCl/CLIVAR workshop to develop priority climate indices. Bulletin Am. Met Soc. 84. 1403-1407. -

Folland. CK.. Karl, T.R., Christy,J.R.. Oarke, R.A., Crouza. G.V .. jouzel,J .. Mann. M.E.. Oerlemans,j., Salinger, M.J .. Wang. S.W .. 2001. Observed climate variability and change. In: Houghton, J.T., et al. (Eds.). Oimate Change 2001: The Scientific Basis-Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climatic Change. Cambridge Univ. Press. New York, pp. 85-97.

Frich, P .. Alexander. LV~ Della-Marta, Gleason. B .. Haylock, M .. Klein Tank, A.M.G .. Peterson, T .. 2002. Observed coherent changes in climatic extremes during rhe 2nd half of the 20th century. Clim. Res. 19. 193-212.

Gopinathan, C.K .. 2000. An indirect method for forecasting the annual food produc­tion oflndia. Curr. Sci. 79 (1 ), 23-24.

Goswami. B.N .. Venugopal, V .. Sengupta, D .. Madhusoodanan. MS .. Prince. K. Xavier. 2006. Increasing trend of extreme rain events over India in a warming environ­ment. Sci. 314. 1442-1444.

Groisman, P.Y .. Knight, R.W .. J<arl . T.R., Easterling, D.R., Sun. B., Lawrimore.j.H .. 2004. Contemporary changes of the hydrological cycle over the contiguous United States: trends derived from in siru observations.) . Hydromet. 5. 64-85.

Griffiths, G.M .. Salinger. M.J .. Leleu. I .. 2003. Trends in extreme daily rainfall across the South Pacific and relationship to the South Pacific Convergence Zone. lnt.J. Oimatol. 23, 847-869.

Guhathakurta. P., Rajeevan. M., 2007. Trends in the rainfall pattern over India. Int. J. Climatol. 28, 1453-1469, doi: 10.1002/joc. 1640.

Hansen, J .. Ruedy, R., Sato, M., Imhoff, M .. Lawrence, W .. Easterling. D .. Peterson. T .. Karl. T .. 2001. A closer look at United States and global surface temperature change.]. Geophys. Res. 106 (23). 947-1023, 963.

Haylock. M.R., Goodess. C.M .. 2004. lriterannual variability of extreme European winter rainfall and links with mean large-scale circulation. Int J. Climatol. 24. 759-776. ..

Haylock, M.R.. Peterson, T.C.,Alves. LM .. Ambrizzi. T .. Anunciacao, Y.M.T .. Baef,J .. Bar­ros, V.R .. Berlato. MA. Bidegain, M., Coronel. G .. Corradi, V .. Garcia, VJ .. Grimm, A.M .. Karoly, D .. Marengo, JA, Marino. M.B .. Moncunill. D.F .. Nechet, D .. Quin­tana. J .. Rebello, E., Rusticucci, M .. Santos. J.L, Trebejo, I., Vincent. LA .. 2006. Trends in total and extreme south American rainfall 1960-2000 and links with sea surface temperature.). Clim. 19, 1490-1512.

Inter-governmental Panel on Climate Change (IPCC), 2007. Climate Change 2007: Impacts. Adaptations and Vulnerability. Cambridge University Press. UI<.

jearakongman. S .. Rajatasereekul, S .. Naklang. K., Romyen, P .. Fukai. S .. Skull<hu. E .. jumpaket, B .. Na thabutr, K., 1995. Growt h and grain productivity of contrasting rice cultivars grown under different conditions of water availability. Field Crops Res. 44, 139-1 SO.

Jones. P.D .. Moberg, A., 2003. Hemispheric and large-scale surface air temperature variations: an extensive revision and an update to 2001. J. Clim. 16, 206-223.

•

N. Subash ec al. / Agriculcural Wacer Management 98 (2011) 1373-1387 1387

Joshi. V.R .. Rajeevan M., 2006. Trends in precipitation extremes over India. NCC Research Report 3, IMO.

Kandiannan. K., Chandaragiri, K.K., Sankaran, N .. Balasubramanian, T.N., l<ailasan, C., 2002. Crop-weather model for turmeric yield forecasting for Coimbatore district, Tamil Nadu, India. Agri~ Forest Meteorol. 112, 133-137.

Khan. M.A .. Singh, l<.M., Singh, S.S .. Upadhyaya, A., Srivastava, T.l< .. l<umar, U .. Dey, A .. Subash. N .. 2009. Bihar Agricultural Contingency Plan. Mimeograph M-01/PAT-01/2009' pp. 1-27.

Klein Tank. A.M.G., Konnen, G.P .. 2003. Trends in indices of daily temperature and precttation extremes in Europe, 1946-1999.J. Clim. 16, 3665-3680.

Klein Tan . A.M.G., Peterson, T.C., Quadir, D.A., Dorji, S. Zou, X., Tang. H .. Santhosh, K .. Joshi, U.R.. Jaswal. A.K., Kolli, R.K .. Sikder. All .. Deshpande, N.R., Revadekar. J.V .. Yeleuova, K .. Vandasheva, S., Faleyeva, M .. Gomboluudev, P., Budhathoki, K.P .. Hussain, A., Afzaal, M .. Chandrapala, L, Anvar, H .. Amanmurad, 0., Asanova, V.S .. Jones, P.O., New .. M.G., Spektorman. T., 2006. Changes in daily air tempera­ture and precipitation extremes in central and south Asia.j. Geophys. Res. 111, 016105. doi:10.1029/2005JD006316.

Kundzewia.Z.W .. Robson, A .. 2000. Detecting trend and other changes in hydrologi­cal data. World Climate Program-Water, WMO/UNESCO, WCDMP-45, WMO(TD l 013, Geneva, p. 157.

Kunkel, K.E.. Easterling, D.R .. Redmond, K.. Hubbard, K .. 2003. Temporal variations of extreme precipitation events in che United States: 1895-2000. Geophys. Res. Lett. 30 ( 17), 1900, doi: 10.1029/2003Gl018052.

Manton, M.)., Della-Marta, P.M .. Haylock, M.R .. Hennessy, K.J .. Nicholls, N .. Cham­bers. LE .. Collins, D.A .. Daw, G .. Finet, A .. Gunawan, D .. !nape, K., lsobe, H .. Kestin, T.S .. Lefale, P., Leyu, C.H .. Lwin. T .. Maitrepierre, L. Ouprasitwong, N .. Page, C.M., Pahalad. j .. Plummer, N., Salinger. M.j., Suppiah. R .. Tran. V.L. Trewin. B .. Tibig, I., Yee, D., 2001. Trends in extreme daily rainfall and temper­ature in southeast Asia and the South Pacific: 1961-1998. In t. J. Climatol. 21, 269-284.

Mokssit, A., 2003. Development of priority indices for Africa: a CCl/CLIV AR workshop of the World Meteorological Organization. In: Bolle, HJ. (Ed.). Mediterranean Climate: Variability and Trend.s. Springer, New York, pp. 116-123.

NATCOM, 2004. lndia's Initial National Communication to the United Nations Frame­work Convention on Climate Change. National Communication Project, Ministry of Environment and Forests, Govt. of India.

New, M .. Todd, M., Hulme, M .. Jones, P., 2001. Precipitation measurements and trends in the twentieth century. Int). Climatol. 21(15),1889-1922.

Ortiz. R .. Sayre. K.D .. Govaerts. B .. Gupta. R .. Subbarao. G.V .. Ban,J .. Hodson. D .. Dixon, J.M .. Ortiz-Monasterio, j.l., Reynolds, M., 2008. Climate change: can wheat beat the heat? Agric. Ecosys. Environ. 126. 46-58.

Pal. I., Al-r.bbaa, A., 2010. Regional changes in extreme monsoon rainfall deficit and excess in India. Dyn.Atmos. Oceans 49. 206-214.

Peterson. T.C.. Vose, R.S., 1997. An overview of the global historical climatology network temperature data base. Bull. Am. Met. Soc. 78, 2837-2849.

Peterson. T.C., Taylor, M.A., Demeritte, R .. Duncombe, D.L, Burton. S .. Thompson. F., Porter, A, Mercedes. M .. Villegas, E .. Semexant Fils. R .. Klein T;ink. A.. Martis, A, Warner, R.,Joyette, A., Mills. W .. Alexander, L. Gleason, B .. 2002. Recent changes in climate extremes in the Caribbean region.J. Geophys. Res. 107 (021), 4601 . doi: 10.1029/2002JD002251.

Peterson, T.C., 2005. The workshop on enhancing south and central Asian climate monitoring and indices, Pune, India, February 14-19, 2005. CLIVAR Exch .. 10. 6.

Peterson, T.C., Folland, C., Gruza, G. Hogg, W .. Mokssit, A, Plummer. N .. 2001. Report of the Activities of the Working Group on Climate Change Detection and Related Rapporteurs. Tech. Doc. 1071, World Meteorological Organization, Geneva. Switierland, 146 pp.

Rajeevan. M .. Bhate.J .. Jaswal, A.K.. 2008. Analysis of variabilify and trends of extreme rainfall events over India using 104 years of gridded dai ly rainfall data. Geo phys. Res. Lett. 35, L18707, doi:10.1029/2008GL035143.

Sen Roy, S .. Balling Jr .. R.C.. 2004. Trends in extreme daily precipitation on indices in India. Int J. Climatol. 24, 457-466.

Sensoy, S .. Peterson, T.C .. Alexander. LV .. Zhang. X .. 2006. Middle east climate change monitoring'and indices workshop summary. Bull. Am. Met. Soc ..

Sneyers, R .. 1990. On the statistical analysis of series ofobservations. WMOTechnical Note No. 143, Geneva.

Serrano, A.. Mateos. V.L. Garcia. JA. 1999. Trend analysis for monthly precipita­tion overthe Iberian Peninsula for the period 1921-1995. Phys. Chem. Earth 24. 85-90.

Su bash, N .. R.am Mohan. H.S .. Sikka, AK .. 2009. Quantitative assessment of influence of monsoon rainfall variability on rice production over India. J. Agrometeorol. 11 (2), 109-116.

Subash. N .. Ram Mohan, H.S.. 2010a. Trend detection in rainfall and evaluation of standardized precipitation index as a drought assessment index for rice-wheat productivity over IGR in India. Int. J. Climatol.. doi: 10.102/joc.21'88.

Subash. N .. Ram Mohan, H.S., 201 Ob. An investigation into observational charac­teristics of rainfall and temperature in Central Northeast India- a historical perspective 1889-2008. Theor. Appl. Climatol.. doi:10.1007/s00704-010-0299-2.

Vincent, LA .. Peterson, T.C., Barros, V.R .. Marino, M.B .. Rusticucci. M .. Carrasco. G .. Ramirez, E., Alves, M .. Ambrizzi, T .. Berlato. M.A.. Grimm. A.M .. Marengo. A.. Molion. L, Moncunill, D.F., Rebello, E., Anunciacao, M.T .. Quintana.J .. Santos.J.L. Baez, j.. Coronel, G. Garcia, j .. Trebejo. l., Bidegain. M .. Haylock. M.R .. Karoly, D .. 2005. Observed trends in indices of daily temperature extremes in South America 1960-2000. J. Clim 18, 5011 - 5023. .

Yoshida. S., 1975. Factors that limit growth. In: Major Research in Upland Rice .. IRRJ, Los Banos, Philippines, pp. 46-71.

Yoshida. s. 1978. Tropical climate and its influence on rice. lRRI Research Paper Series No. 20. JRRl, Los Banos, Philippines.

Zhang, X .• Heger!, G .. Zwiers. F.W .. Kenyon, J .• 2005. Avoiding inhomogeneity in percentile-based indices of temperature extremes.J. Clim. 18, 1641-1651.

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