Estimating sowing and harvest dates based on the Asian summer monsoon

30 Pages·2017·9.21 MB·English

EarthSyst.Dynam.,9,563–592,2018
https://doi.org/10.5194/esd-9-563-2018
©Author(s)2018.Thisworkisdistributedunder
theCreativeCommonsAttribution4.0License.
Estimating sowing and harvest dates based on the
Asian summer monsoon
CamillaMathison1,ChetanDeva2,PeteFalloon1,andAndrewJ.Challinor2
1MetOfficeHadleyCentre,FitzRoyRoad,Exeter,EX13PB,UK
2SchoolofEarthandEnvironment,InstituteforClimateandAtmosphericScience,
UniversityofLeeds,Leeds,LS29AT,UK
Correspondence:CamillaMathison(camilla.mathison@metoffice.gov.uk)
Received:12October2017–Discussionstarted:1November2017
Revised:14March2018–Accepted:25April2018–Published:18May2018
Abstract. Sowing and harvest dates are a significant source of uncertainty within crop models, especially for
regions where high-resolution data are unavailable or, as is the case in future climate runs, where no data are
availableatall.Globaldatasetsarenotalwaysabletodistinguishwhenwheatisgrownintropicalandsubtropical
regions,andtheyarealsooftencoarseinresolution.SouthAsiaisonesuchregionwherelargespatialvariation
means higher-resolution datasets are needed, together with greater clarity for the timing of the main wheat
growingseason.AgricultureinSouthAsiaiscloselyassociatedwiththedominatingclimatologicalphenomenon,
the Asian summer monsoon (ASM). Rice and wheat are two highly important crops for the region, with rice
beingmainlycultivatedinthewetseasonduringthesummermonsoonmonthsandwheatduringthedrywinter.
WepresentamethodforestimatingthecropsowingandharvestdatesforriceandwheatusingtheASMonset
andretreat.Theaimofthismethodistoprovideamoreaccuratealternativetotheglobaldatasetsofcropping
calendarsthaniscurrentlyavailableandgeneratemorerepresentativeinputsforclimateimpactassessments.
Wefirstdemonstratethatthereisskillinthemodelpredictionofmonsoononsetandretreatfortwodownscaled
generalcirculationmodels(GCMs)bycomparingmodelledprecipitationwithobservations.Wethencalculate
and apply sowing and harvest rules for rice and wheat for each simulation to climatological estimates of the
monsoononsetandretreatforapresentdayperiod.Weshowthatthismethodreproducesthepresentdaysowing
andharvestdatesformostpartsofIndia.Theapplicationofthemethodtotwofuturesimulationsdemonstrates
thattheestimatedsowingandharvestdatesaresuccessfullymodifiedtoensurethatthegrowingseasonremains
consistent with the internal model climate. The study therefore provides a useful way of modelling potential
growingseasonadaptationstochangesinfutureclimate.
1 Introduction pact Model Intercomparison Project (ISIMIP; Warszawski
etal.,2013,2014),andtheGlobalGriddedCropModelInter-
Fieldstudiesdominatethemodellingliteratureoncropsand comparison (GGCMI; Elliott et al., 2015). Recent work in
agriculture.Manycropmodelsaredevelopedandappliedat suchclimate–cropimpactstudieshassoughttoquantifyun-
thefieldscaleusingsite-specificobservationstodrivemod- certainty from the quality and scale of input data. A result
elsandoptimizeoutputs.Thegrowingawarenessofclimate from this work is that for global-scale simulations, planting
changeandthelikelyimpactthiswillhaveonfoodproduc- dates are a significant source of uncertainty (Frieler et al.,
tionhasgeneratedademandforregionalandglobalassess- 2017;Elliottetal.,2015).
ments of climate impacts on food security through, for ex- Asidefromtheiruseinmodellingstudies,decidingwhen
ample,projectssuchastheAgriculturalModelIntercompar- toplantcropsisasignificantchallenge,particularlyinwater-
isonandImprovementProject(AgMIP;RivingtonandKoo, scarce regions such as parts of sub-Saharan Africa (SSA;
2010;Rosenzweigetal.,2013,2014),theInter-SectoralIm-
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
564 C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon
Waongoetal.,2014)andSouthandSouth-eastAsia(Kotera practices is not really suitable for adaptation studies (van
et al., 2014). These regions have crop sowing dates that are Bussel et al., 2015). The assumption that there will be no
closely associated with the onset of the rainy season. Any large shifts in climate causing sowing and harvest dates to
prolongeddryspellsofmorethan2weeksaftersowingcould change significantly from the present day could lead to the
haveseriousconsequencesleadingtocropfailureorsignifi- sowing and harvesting of crops in the model in the future
cantyieldreductionbecausetopsoillayersdryout,prevent- at unrealistic times of the year. Thus, the appropriate sow-
inggermination(Lauxetal.,2008).ForlargepartsofSSA, ingandharvestdatesusedinfuturesimulationsdependson
decidingwhentosowdeterminesthelengthofthecropdura- the intended application for the simulations. In many adap-
tionfortheagriculturalseasonandisthereforeanimportant tation studies, impacts without adaptation are assessed us-
tacticaldecision(Waongoetal.,2014). ing present day estimates of sowing dates, then the sowing
Planting dates can be determined using a number of dif- datesareadjustedinresponsetoclimatechangetoassessthe
ferent methods; for example, Kotera et al. (2014) propose a benefitsofadaptation(Lobell,2014).Challinoretal.(2017)
cropping calendar model for rice cultivation in the Vietnam suggest using autonomous adaptation in simulations in or-
Mekong Delta (VMD). The Kotera et al. (2014) model es- dertoavoidoverestimatingtheeffectsofadaptation.Onthis
timates the sowing date based on the suitability of the land basisthereisarequirementforestimatesofsowingandhar-
for crops given any flooding, saltwater intrusion, or erratic vestdatesforclimatesimulationsthatremainconsistentwith
monsoonrains;these areimportantfactorsfor thewaterre- the future model climate. This makes estimates of sowing
sourcesoftheVMDregion.Alternatively,Lauxetal.(2008, and harvest dates important not only for understanding the
2010) use a fuzzy-logic-based algorithm developed to esti- presentday,butalsoforuseinfuturesimulations,especially
mate the onset of the rainy season in order to examine the whenconsideringpotentialadaptationtoclimatechange.
impactoftheplantingdatefortheSSA.IntheGeneralLarge AgricultureinSouthAsiaisdominatedbytheAsiansum-
Area Model (GLAM; Challinor et al., 2004a), the sowing mermonsoon(ASM).KharifandRabiarethetwomainsea-
date can be estimated by the model based on the soil mois- sonsinSouthAsianagricultureandthesecorrespondtosum-
tureconditions,withthecropsownwhensurfacesoilmois- mer and winter–spring growing seasons respectively. Rice–
ture exceeds a specified threshold during a given time win- wheat systems are a major crop rotation across South Asia.
dow and crop emergence occurring at a specified time after Kharif crops include rice, which is usually sown during the
sowing. Waha et al. (2012) base their estimates of sowing monsoon and harvested in the autumn. Sowing and harvest
dates at the global scale on climatic conditions and crop- dates for rice cultivated during the Kharif season vary be-
specific temperature thresholds, therefore providing a suit- tween states, with rice traditionally sown in some locations
able method for taking climate change into account. How- withthefirstrainsofthemonsoon,whileotherregionssuch
ever, the Waha et al. (2012) method is not really intended aseasternpartsoftheIndo-GangeticPlain(IGP)tendtoplant
for use in irrigated multiple cropping regions. Elliott et al. rice late into June when the monsoon is fully established
(2015)describehowsowingdatesaredefinedintheGGCMI (Erenstein and Laxmi, 2008). Rabi crops include wheat,
project. The GGCMI protocols use a combination of Sacks which is mainly cultivated during the dry season (Erenstein
et al. (2010), Portmann et al. (2010), and model data to de- andLaxmi,2008;Singhetal.,2014).Thecloseassociation
finesowingdates,thushighlightingthechallengesindefin- of the sowing dates of these crops and the ASM offers the
ingacomplete,accuratedatasetofsowingandharvestdates. potentialforanewmethodofdefiningthecroppingcalendar
Thishasinfluencedanddriventhedevelopmentandapplica- forthisimportantrotation.
tion of crop models on broader scales. In this study we are Rice–wheatsystems,particularlythoseinPakistan(Eren-
consideringthewholeSouthAsiaregion;thisisalarge-scale steinetal.,2008)andtheIndo-GangeticPlain(IGP),tendto
problem with complicated cropping patterns, which means plant varieties like Basmati that take a long time to mature
thatassumptionsandgeneralizationsneedtobemadeacross (ErensteinandLaxmi,2008).Sincethisdelayswheatplant-
aregionwithawidevarietyofclimaticconditionsandcrop- ing,thishasadirectimpactonwheatyield.IntheeasternIGP
ping environments (soils etc). Waha et al. (2013) highlight thisisaparticularproblemastheseasonforwhichwheatis
thefactthatglobalcropcalendarssuchasthoseusedinthe viable is relatively short (Erenstein and Laxmi, 2008; Laik
GGCMIoftenonlyreportindividualcrops,thereforelimiting et al., 2014; Jat et al., 2014). Any delay between the rice
theirusefulnessforregionswithmultiplecroppingsystems. harvest and wheat planting can have a large impact on the
The growing interest in climate change and food secu- successofthewheatcropasthiswillreducethetimeavail-
rity has influenced the development of crop models for use able before the temperatures get too high for the successful
in future climate impact assessments (Frieler et al., 2017); cultivation of wheat (Joshi et al., 2007). The time between
thisrepresentsadifferentchallengeforcropmodelsinterms thericeharvestandwheatsowingalsodependsonthetime
oftheinputdataused.ISIMIPsimulationsusetime-varying ittakestoensurethesoilisinasuitableconditionforwheat
crop management data until 2005, after which the data are sowingafterthericeharvest.ErensteinandLaxmi(2008)de-
heldfixedat2005levelsfortheremainderofthesimulations scribethezero-tillageapproach,whichallowsforareduced
(Frieleretal.,2017).Fixingcropmanagementtopresentday turnaround time between the harvest of rice and sowing of
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C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon 565
wheat.Potentialavenuesbywhichtheuncertaintyfromsow- integrated effective temperature in each development stage
ingandharvestdatescanbereducedininputstocropsimu- is referred to as the thermal time of that development stage
lationsinclude (Cannell and Smith, 1983; McMaster and Wilhelm, 1997);
there may also be an additional photoperiod length depen-
– the use of higher-resolution regional datasets of
dence. The thermal time in each development stage is typ-
recorded sowing and harvest dates for crop calendars
ically set by the user and can be calibrated to simulate dif-
ratherthanexistingglobaldatasets;and
ferentvarietalproperties.Wherethesevarietalpropertiesare
unavailable, e.g. for the global analysis in Osborne et al.
– theuseofnewmethodsforestimatingcropcalendarsin
(2014), in order to mimic the spatial variation in the choice
theabsenceofhigher-resolutionregionaldatasets.
of crop variety, these thermal times were determined from
sowing and harvest dates and the temperature climatology,
whichallowedthemtovaryspatially.Thisensuresthatdur-
Motivation
ing the simulation, the crop develops over the course of the
The correct representation of the crop duration within crop cropseasonstartingatthesowingdateandendingatapprox-
modelsiscrucialfortheinterpretationoftheimportantout- imately the harvest date (i.e. the harvest date is the average
puts from the model. For example, if the datasets used for over the course of the climatological period used). The use
sowing and harvest dates are inaccurate, the simulations of this predefined thermal time ancillary drives the require-
could grow crops during the wrong season, thereby affect- mentforprovidingbothasowingandharvestdate.Reliable
ingthereliabilityofthesimulatedwateruseandcropyield. high-resolutiondatasetsforsowingandharvestdatesareof-
ThemaindifferencesbetweentheregionalBodhetal.(2015) ten unavailable for either the region or the time period that
datasetandtheglobalSacksetal.(2010)dataareforspring is needed. In addition, there is a demand for sowing and
wheat.Springwheatgrowninwinterismisclassifiedaswin- harvest dates that maintain consistency with the model cli-
terwheatintheSacksetal.(2010)data.Thisisdiscussedby mate. Therefore, in this paper we propose a new method,
Sacks et al. (2010) as a potential limitation when using the outlined in Fig. 2, for estimating sowing and harvest dates
datafortropicalandsubtropicalregions.Springwheatisthe for use in the large-scale modelling of the rice–wheat ro-
morecommontypeofwheatgrownintheSouthAsiaregion tation in South Asia using estimates of monsoon onset and
(Hodson and White, 2007) because minimum temperatures retreat. This method does not require large amounts of data
therearenotlowenoughtoallowvernalizationtotakeplace, andtheusercanelecttouseeitherthesowinginputdataor,
which is needed for winter varieties of wheat (Sacks et al., if needed, both sowing and harvest data to run their chosen
2010;Yanetal.,2015). cropmodel.Themainobjectivesofthisstudyare
Figure 1 shows the averaged rice (green rectangles)
– todevelopamethodfordeterminingsowingandharvest
and wheat (orange rectangles) growing season durations
dates for modelling the rice–wheat rotation in South
for Sacks et al. (2010) (diagonal hatching) and the Bodh
AsiabasedontheASMand
etal.(2015)dataset(perpendicularhatchinglabelledMinAg)
overlaidonthepresentdaySouthAsiaaveragedprecipitation – totestthemethodincurrentandfutureclimates.
climatologyandestimatesofthemonsoononsetandretreat.
ThisillustratesthedifferencesbetweentheBodhetal.(2015) We therefore present the methodology in Sect. 2. We show
andSacksetal.(2010)datasets,showingthatinSacksetal. that the proposed method is viable and show that it works
(2010)themaingrowingperiodforbothriceandwheatap- in Sect. 3. A discussion of the results and conclusions are
pearstobeduringthemonsoon.Whilericeisusuallygrown providedinSects.4and5respectively.
during the monsoon it is not typical that wheat should be
grown during this period for this region. The growing sea- 2 Methodology
son durations for the Bodh et al. (2015) dataset (see Fig. 1,
perpendicularhatchingrectangleslabelledMinAg)aremore ThemethodologyissummarizedintheflowchartinFig.2.
typical of this region with rice (green) growing during the The model datasets, described in detail in Appendix A, in-
monsoonandwheat(orange)growingduringthedryseason. clude general circulation models (GCMs) and a regional
Figure 1 highlights that where a global dataset is unable to climate model (RCM). GCMs provide spatially consistent
establishexactlywhenwheatisgrownintropicalregions,an boundary data to an RCM, which generates 25km regional
alternativeisneeded. fields (see Fig. 2 blue boxes). The two GCMs used in this
Crop models such as those described by Challinor et al. analysis were specifically selected because they were able
(2003, 2004b) and Osborne et al. (2014) require sowing in- tocapturethemainfeaturesoftheASM(seeAppendixA).
formation such as a sowing date or a sowing window, with RCMs are based on the same physical equations as GCMs
thecropmodelintegratinganeffectivetemperatureovertime and therefore represent the entire climate system, including
asthecropdevelops.Theeffectivetemperatureisafunction thecarbonandwatercycle.Theirhigherresolutionallowsfor
of air or leaf temperature and differs between models. The abetterrepresentationoftheregional-scaleprocesses,adding
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566 C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon
1990–2007 RCM climatology: precipitation for South Asia
7
6
5
4
1−
y
a
d
m
m

3
2
1
0
21 41 61 81 101121141161181201221241261281301321341361 16 36 56 76 96 116
Time / day of year
HadCM3 HadCM3_onset ECHAM5_onset Minag_obs_rice
APHRODITE HadCM3_retreat ECHAM5_retreat Minag_obs_wheat
ECHAM5 APHRODITE_onset ERAint_onset Sacks_obs_rice
ERAint APHRODITE_retreat ERAint_retreat Sacks_obs_wheat
Figure1.The1.5-yearprecipitationclimatologyforthe1990–2007periodaveragedforSouthAsiaforeachsimulation(ERA-Interim(la-
belled“ERAint”inthefiguresthroughoutthepaper)cyanline,ECHAM5blueline,HadCM3redline)andAPHRODITEobservations(black
line)usinga5-daysmoothedrollingmean.Alsoshownarethegrowingseasonsaveragedfor1990–2007forSouthAsiaforwheat(orange)
andrice(green)fromtwodatasets:Sacksetal.(2010)(diagonalhatching-labelledsacks)andBodhetal.(2015)(perpendicularhatching-
labelled Minag) and the monsoon onset (blue vertical lines) and retreat (pink vertical lines) from each of the simulations (APHRODITE
dotted,ERA-Interimdashed,HadCM3solid,ECHAM5dashdot).
detailtofieldslikeprecipitation(Mathisonetal.,2015).The sensible results; these are described in Sect. 2.1 and shown
individualRCMsimulations(alsocalledHNRCMS;seeAp- bythegreenboxes.
pendix A) used in this analysis are referred to using their
globaldrivingdataabbreviations:HadCM3,ECHAM5,and
2.1 Observations
ERA-Interim (labelled “ERAint” in the figures throughout
the paper) as described in Appendix A. Precipitation fields In order to demonstrate the viability of the methodology
are used to generate a precipitation climatology, which is outlined in Fig. 2, we compare the simulated precipitation
usedtocalculatethemonsoonstatistics(seeSect.2.2)from with observations from the Asian Precipitation-Highly Re-
whichsowingandharvestdatesareestimated;shownbythe solved Observational Data Integration Towards the Evalua-
pinkrectangles(seeSect.2.3). Theseestimatedsowingand tionofWaterResources(APHRODITE;Yatagaietal.,2012)
harvestdatesarereferredtoasrelativemonsoonsowingand datasetinSect.2.2.1.APHRODITEisadaily,0.25◦ resolu-
harvestdates(seeFig.2).Observationsareusedthroughout tion land-only gridded dataset that is also used in Mathison
theprocesstoensurethatthemethodisviableandproduces etal.(2015)toshowthattheRCMsinthisanalysiscapture
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C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon 567
Figure2.Aflowchartsummarizingthemethodology.Thebluerectanglesrepresentdatasetsthatareusedwithinthemethodology,green
rectanglesrepresentobservations,andpinkrectanglesrepresentanycalculationpartsofthemethodology.
thegeneralhydrologyoftheregion.Themonsoonisahighly izedPentadPrecipitationIndex(NPPI)(Lucas-Picheretal.,
variableandcomplexphenomenonthatcurrentlynotallcli- 2011). The NPPI and Sperber et al. (2013) methods both
matemodelsareabletorepresent;thismaymeanthatsome use a long-term climatological average of precipitation be-
climate models would not yet be suitable for use with this cause the model data are too noisy to calculate the mon-
method, which relies on a good representation of the mon- soon statistics per year. Agricultural-specific definitions of
soon.ThemethodpresentedinFig.2willbecomemorero- monsoon onset and retreat represent breaks in the monsoon
bust with improving representations of the monsoon in cli- which can adversely affect the germination of crops. How-
matemodels. ever, these metrics are not as effective when used in con-
The datasets used for sowing and harvest dates include a junction with long-term average precipitation fields such as
global dataset, Sacks et al. (2010), and a regional dataset, those used here. This is probably because the breaks that
Bodh et al. (2015), from the government of India, Ministry occur in the monsoon are quite variable from year to year
ofAgricultureandFarmersWelfare.TheBodhetal.(2015) andaresmoothedoutwithintheclimatology.Theapproach
data are referred to from here on as MinAg data. The Mi- by Sperber et al. (2013) defines monsoon onset as the pen-
nAg observations of sowing and harvest dates for rice and tadinwhichtherelativerainfallexceeds5mmday−1 during
wheataregivenasarangeofdaysofyear.Themidpointsof the May–September period. However, Sperber et al. (2013)
these observed ranges are calculated and compared against regrid to the GPCP rainfall dataset (Huffman et al., 2001),
the midpoints of the model pentads for onset and retreat in whichismuchcoarserresolutionthantheAPHRODITEdata
dayofyear.Asapost-processingstepthedifferencesarethen usedhere.TheNPPImetricusesEq.(1)toestimatemonsoon
masked using crop areas from the International Crops Re- onset,retreat,peak,andduration.
searchInstitutefortheSemi-AridTropics(ICRISAT,2015) P −P
sothatonlytheareaswherericeorwheatisgrownarecon- NPPI= min , (1)
P −P
sidered. max min
whereP istheunsmoothedpentadprecipitationclimatology
and P and P are the annual minimum and maximum
2.2 Estimatingmonsoononsetandretreat min max
ateachgridboxrespectively.Themonsoononsetisthende-
There are a wide variety of metrics for estimating the mon- finedasthepentadinwhichtheNPPIexceeds0.618forthe
soon onset and retreat. Some are specific to agriculture and firsttimeandwithdrawalasthelasttimetheNPPIdropsbe-
include a representation of breaks in the monsoon (Moron lowthisthresholdintheyear.TheNPPIonlyreachesavalue
andRobertson,2014).Moregeneralmetricsincludeacom- of 1.0 once in the annual cycle, which corresponds to the
bination of meteorological variables, such as 850hPa wind monsoonpeak.IntheNPPImethodtheonlyregriddingthat
andprecipitationasinMartinetal.(2000),oronlyusepre- takes place is to ensure that the model and observations are
cipitation, such as in Sperber et al. (2013) and the Normal- on the same grid; as they are both 25km resolution there is
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568 C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon
nolossofresolutionindoingthis.ThethresholdforNPPIis methodallowsanycropmodelthatuses,forexample,adriv-
also independent of the resolution of the data, which is not ing dataset similar to APHRODITE or the HNRCMs to de-
thecasefortheSperberetal.(2013)method.TheNPPImet- rive sowing and harvest dates that are consistent with the
richasbeensuccessfullyappliedpreviouslybyLucas-Picher monsoon of the driving data (see Fig. 2). Thus, the crop is
et al. (2011) to analyse the monsoon in models of a similar grownattheappropriatetimeoftheyear;i.ericeiskeptdur-
resolution to the simulations used here (see Fig. 2). There- ing the monsoon period and wheat is sown and harvested
foreinthisanalysisinthesamewaythatLucas-Picheretal. during the dry season. The monsoon is a highly variable
(2011)usethe1981–2000climatology,weusea1990–2017 phenomenon; however, the use of a long-term average (cli-
climatology. The pentad provided by the NPPI is represen- matology) to calculate the monsoon statistics smooths out
tative of the climatological period and therefore cannot be their large inter-annual variability. This highlights the con-
compared to a particular year; however, the pentad can be sistencybetweenthesowingandharvestdatesandthemon-
usedtofindthe5-daywindowfortheclimatologicalperiod soon statistics. Therefore we do not expect the monsoon
during which onset and retreat typically occur, which can statisticstobeexactlythesameastheobservedsowingand
then be compared to APHRODITE observations also aver- harvest dates. Rather, this method relies on consistency be-
agedforthatperiod.WeusetheNPPImetrictocalculatethe tween the climatological estimate of the monsoon statistics
pentadofthemonsoononset,retreat,peak,anddurationfor and the sowing and harvest dates across the region. The in-
theAPHRODITEobservationsandthethreeHNRCMsimu- troductionofacroprulethenmovesthemonsoonstatisticto
lations. more closely reflect the observed sowing and harvest dates.
Thismeansthatevenifthedifferencebetweenthemostrel-
evant monsoon statistic and the observed sowing or harvest
2.2.1 Comparisonofmodelmonsoononsetandretreat
date is large then the difference is similar across India. Al-
withprecipitationobservations
though these sowing and harvest events may not always be
Figure 3 shows plots of the onset (left column) and the re- dictatedentirelybythemonsoon,thephenomenonprovides
treat(rightcolumn)oftheSouthAsiansummermonsoonas the broader seasonality associated with the crop seasons in
definedusingtheNPPIdescribedinSect.2.2.TheNPPIsfor this region. The consistency betweenthe crop practices and
the climatology of the APHRODITE precipitation observa- themonsoonstatisticsacrosstheregionprovidestheempir-
tions (Yatagai et al., 2012) are shown in Fig. 3a and b for ical relationship exploited here to estimate the sowing and
comparison with the precipitation climatology for each of harvestdatesforuseinbothpresentdayandfuturecropsim-
the HNRCMs shown: ERA-Interim (c and d), HadCM3 (e ulations. These sowing and harvest dates are not really in-
and f), and ECHAM5 (g and h). The white regions are ar- tendedtoofferadvicetofarmersonwhentosoworharvest
easwherethethresholdwasexceededatthefirstpentad;this onayeartoyearbasis;rather,itprovidesawayforsowing
impliesthatthemonsoonhadalreadystartedatthefirstpen- andharvestdatestoremainrelevanttothismajorclimatolog-
tad,whichsuggestsamodelbiasandthereforetheseregions ical feature. A key assumption is that the monsoon remains
weremaskedout.Figure4showsthedifferencesbetweenthe a defining feature of the crop seasons for South Asia in the
model onset (retreat) and APHRODITE onset (retreat) for future.
eachmodel.Onaveragethedifferencebetweenthemonsoon
onset in APHRODITE and the HNRCM simulations is be- 2.3.1 Calculationofmonsoon-derivedestimatesof
tween1and7daysandthedifferencebetweentheretreatin sowingandharvestdatesforriceandwheat
APHRODITEandtheHNRCMsimulationsisbetween4and
10days.However,thereareregionswherethedifferencesbe- We use the precipitation climatologies from APHRODITE
tweentheAPHRODITEmonsoonstatisticsaremuchlarger precipitationobservationsandeachoftheHNRCMsimula-
thanthis;thesearehighlightedbythedarkerredandbluere- tions (see Fig. 2) by calculating the difference between the
gionsinFig.4.IngeneralformostofIndiatheHNRCMSare monsoononset(orretreat)andtheobservedMinAgsowing
within25daysoftheAPHRODITEobservations,withthere- (orharvest)datesforeachcrop(seeFig.2).Thesedifferences
gionswherethedifferencesarelargerexplainedbydifferent arepergridbox.Wethencalculateaweightedareaaverage
monsooncharacteristics,forexamplethesouthofIndiaand (usingtheMetOffice,2018,package)toproduceacroprule
theBangladeshregion(thisisdiscussedfurtherinSect.4.1). forthewholeregionforeachcropandstage;thesearelisted
inEq.(2).Collectively,thecroprulesgiveninEq.(2)arere-
ferredtoasRelMonsoon .Thisprovidesasimplerule
croprule
2.3 Calculatingsowingandharvestdatesfrom
that can be applied across the region, even where observa-
monsooncharacteristics
tions are not available. Although calculating a rule per grid
We useestimates ofthe monsoononset andretreat together boxwouldprovideexcellentresultswhereobservationswere
with present day rules on sowing and harvest for rice and available,itwouldlimittheusefulnessofthemethodwhere
wheat, referred to as crop rules, to calculate the sowing observations were not available, which is one of the main
and harvest dates relative to the monsoon (see Fig. 2). This aimsofthisapproach.
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C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon 569
Figure 3.Plotsofthe1990–2007monsoonstatistics;monsoononset(a,c,e,g)andretreat(b,d,f,h).TheAPHRODITEprecipitation
observations(aandb)areshownandthethreemodelsimulations,ERA-Interim(candd),HadCM3(eandf),andECHAM5(gandh),are
calculatedusingtheNPPImetric.Whiteareasaretheregionswherethemodelprecipitationexceedsthethreshold,indicatingthestartofthe
monsoonattheinitialpentad;thisdoesnotimplyearlymonsoonbutmorelikelyamodelbiasintheprecipitationatthislocation.
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570 C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon
Figure4.Plotsofthe1990–2007differencebetweenmodelsimulationsandAPHRODITEobservationsforthemonsoonstatistics;monsoon
onset(a,c,e)andretreat(b,d,f);ERA-Interim(aandb),HadCM3(candd),andECHAM5(eandf)calculatedusingtheNPPImetric.
EarthSyst.Dynam.,9,563–592,2018 www.earth-syst-dynam.net/9/563/2018/
C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon 571
2.4 Demonstrationusingmonsoon-derivedestimatesof
sowingandharvestdatesfortwofutureperiods
RiceSowingCroprule=AreaAverage(MonsoonOnset
The method summarized in Fig. 2 is applied to two future
−RiceSowing)
periodsusingtheECHAM5andHadCM3RCMsimulations
RiceHarvestCroprule=AreaAverage(MonsoonRetreat
(described in Appendix A). Global mean temperatures are
−RiceHarvest) used (within the High-End cLimate Impacts and eXtremes
WheatSowingCroprule=AreaAverage(MonsoonRetreat project,HELIX)todefinethefutureclimateintermsofspe-
cificwarminglevels(SWLs),i.econsideringa2,4,and6◦C
−WheatSowing)
world.Theuseoftimeperiodsismuchmorecommonthan
WheatHarvestCroprule=AreaAverage(MonsoonOnset
SWLs; however, SWLs enable the analysis to focus less on
−WheatHarvest) (2) theclimatescenariosandmoreonwhattheworldwilllook
likeat2,4,and6◦C(Goharetal.,2017).Thiswilldifferde-
TheRelMonsoon isthenappliedtothemonsoonon-
croprule pendingonwhenthethresholdispassed.TheSWLapproach
setandretreatfieldtoprovideanestimateofsowingandhar-
isthereforeabenefitasitmeansthatnewscenariosthatare
vestdatesforriceandwheatbasedonthemonsoon.Werefer
developedaspartofnewmodelintercomparisonprojectscan
totheseestimatesofsowingandharvestdatesas“monsoon-
be compared against older ones from previous projects. Al-
derivedcropdates”forbrevity.
though the older scenarios may not contain the most up-to-
datesocio-economicinformation,theyarenolesslikelythan
thenewerscenarios.Thesimulationsusedhereareforthepe-
MonsoonDerivedCropDate=MonsoonStatistic
riod 1965 to 2100 and therefore only the 2◦C threshold for
−RelMonsoon , (3)
croprule globalmeantemperatureisactuallypassedduringthesesim-
ulations.ForHadCM3thisoccursin2047andforECHAM5,
wheretheMonsoonStatisticcanbemonsoononsetorretreat
2055. Therefore the two future periods used in this analy-
andtheRelMonsoon isoneofthefourcroprulesgiven
croprule
sisare2040–2057and2080–2097.The2040–2057periodis
inEq.(2).
chosenbecauseitincludestheyearthattheglobalmeantem-
The spatial variability of the monsoon-derived sowing
peratureexceeds2◦Cinthetwosimulations,andthe2080–
and harvest dates is accounted for by the monsoon on-
2097 period is chosen because it is furthest into the future
set and retreat in the climatology used to calculate the
in these simulations and therefore likely to show the great-
RelMonsoon . The monsoon-derived sowing and har-
croprule
est warming. The length of the two future analysis periods
vest dates for both the APHRODITE and HNRCM simula-
hasbeenchosenforconsistencywiththeERA-InterimRCM
tions are provided and compared against MinAg observed
simulation,whichisonlyavailablefortheperiod1990–2007.
sowingandharvestdatesinSect.3.2.Thecalculationofthe
Althoughthethresholdof2◦Cisexceededgloballyitisim-
RelMonsoon isbasedonobservationsforIndia(from
croprule
portant to note that the relationship between the projected
MinAg and ICRISAT, 2015) and therefore the analysis for
globalmeanchangeintemperatureandtheregionalclimate
thepresentdayinSect.3.2focusesontheseareas.Because
change in temperature for South Asia is complicated. Heat
most of the South Asia region is dominated by the ASM,
and moisture and how they vary across the globe are not
the RelMonsoon , though tuned using India observa-
croprule
evenly distributed, with land warming faster than the ocean
tions, can be applied to any region dominated by the ASM
(Christensen et al., 2013); therefore the actual temperature
in order to estimate sowing and harvest dates for larger ar-
change experienced in South Asia may be higher than the
eas with a rice–wheat rotation (see Sect. 3.3). The method
globalmeanchange.
doesnotcurrentlyperformaswellforpartsofsouthernIndia
where the climate is influenced by the North-east Monsoon
but could be modified to provide better results for these ar- 3 Results
eas.InSect.3.2,wecomparethemonsoon-derivedestimates
We compare the model monsoon to the monsoon calcu-
of sowing and harvest dates for the period 1990–2007 with
latedfromprecipitationobservationstodemonstratethatthe
theMinAgrangeofsowingandharvestdatestoestablishif
model is able to reproduce the monsoon (see Sect. 2.2.1);
themethodshowninFig.2givesgoodresults.Therearefour
thereforethemethodologysummarizedinFig.2andSect.2
datasetsusedthroughoutthisanalysis:APHRODITEandthe
is viable. In Sect. 3.1 we compare the simulated monsoon
three HNRCMS. Where three of the four datasets provide
with the observed sowing and harvest dates in order to cal-
sowing or harvest dates that are within the MinAg range,
culate the monsoon-derived sowing and harvest dates and
the method is said to give good results; where two of the
comparethesenewsimulatedsowingandharvestdateswith
four datasets are within the MinAg range, the results from
the observations. We then show results from applying the
the method are said to be fair. If no datasets are within the
method in Sect. 3.2. As a demonstration, we also apply the
MinAgrange,themethodisclassedaspoor.Thesowingand
methodtotwofutureperiodsinSect.3.3.
harvestdatesarepresentedforeachstateinSect.3.2.
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572 C.Mathisonetal.:EstimatingsowingandharvestdatesbasedontheAsiansummermonsoon
Table 1. Table of RelMonsooncroprule for each dataset, crop, and a “miss” (shown by the red regions). The yellow regions in
stage. The RelMonsooncroprule is the value subtracted from the Fig. 6 show the places where the monsoon onset overlaps
monsoononsetandretreatinordertocalculateanewsowingorhar- the range of observed sowing days but does not completely
vestdatebasedonthemonsoononsetandretreat.Ineachcasethe fallwithinit;theseregionsarelabelled“overlaps”.Figure6
newestimateofthesowingandharvestdatesiscalculatedbysub-
has only a small area of red indicating that monsoon onset
tractingtheRelMonsooncroprulefromtheMonstat,whereMonstatis is, for large parts of India, within the range of days of rice
monsoononsetormonsoonretreatfromHNRCMorAPHRODITE
sowing. In each plot shown in Fig. 6 the region that is red
precipitation observations. Where the sowing or harvest is before
oryellowisdifferent,andthismakesitdifficulttosayifone
themonsoonstatistic,thecropruleisinboldwithnormaltypein-
datasetisbetterthananother.ECHAM5appearstohavethe
dicatingthatsowingorharvestoccursafterthemonsoonstatistic.
smallest total area in red or yellow, which is probably be-
causeECHAM5tendstohaveanearlieronsetthantheother
Crop Stage Monstat Source RelMonsooncroprule
(Indiaaverage) datasets and in general that makes it closer to the rice sow-
ingdates.Table1liststhedifferencesbetweenthemonsoon
Wheat Sowing Retreat APHRODITE −63.5
Wheat Sowing Retreat ERA-Interim −62.8 statistics(onsetandretreat)andtherelevantsowingandhar-
Wheat Sowing Retreat HadCM3 −67.9 vest dates for each crop calculated for each of the simula-
Wheat Sowing Retreat ECHAM5 −63.6 tions and the APHRODITE observations and averaged for
India.Table1showsthatonaverageacrossIndiaricesowing
Wheat Harvest Onset APHRODITE 98.5
Wheat Harvest Onset ERA-Interim 100.4 occurs between 10 and 20 days prior to the averaged mod-
Wheat Harvest Onset HadCM3 98.9 elled monsoon onset (third block, Table 1). We would not
Wheat Harvest Onset ECHAM5 91.4 expect the different datasets to give the same results; how-
Rice Sowing Onset APHRODITE 19.7 ever, Table 1 shows that they are relatively consistent with
Rice Sowing Onset ERA-Interim 17.3 each otherand, importantly,with observationsas illustrated
Rice Sowing Onset HadCM3 17.2 by the APHRODITE data. Table 1 highlights the fact that
Rice Sowing Onset ECHAM5 10.1
onaverageAPHRODITErequiresalargercroprulethanthe
Rice Harvest Retreat APHRODITE −32.7 simulations for rice sowing; however, this is not always the
Rice Harvest Retreat ERA-Interim −35.4 caseforsowingorharvestandriceorwheat.Thecroprules
Rice Harvest Retreat HadCM3 −38.5
usedherearebasedonthe1990–2007periodforwhichERA-
Rice Harvest Retreat ECHAM5 −34.7
Interimhastheearliestonset(seeFig.10).ECHAM5hasthe
smallestcropruletomoveittowardsthericesowingdatebut
thehighestvarianceinthemeandifferencebetweenthemon-
3.1 Comparingobservedsowingandharvestdateswith soon onset and the MinAg rice sowing date. APHRODITE
estimatesofmonsoononsetandretreat has the largest crop rule for rice sowing, indicating that the
weightedaverageoftheAPHRODITEmonsoononsetisfur-
TheclimatologyinFig.1showsthatonaveragetheobserved therfromthericesowingdatethanforotherdatasets.
rice and wheat sowing and harvest dates from MinAg align In general the differences between rice harvest and mon-
well with the monsoon onset and retreat in the simulations. soon retreat are larger but still consistent across the re-
Observedricesowingdatesgenerallycomparewellwiththe gion (see Fig. B1), with rice harvest occurring on aver-
monsoononsetinthemodelasshowninFigs.5and6. age 30–40 days after monsoon retreat (see fourth block,
The monsoon onset and retreat estimates are provided Table 1). Wheat sowing tends to occur approximately 60–
indaysofyear(pentads)andthereforewitharangeofplus 70 days after monsoon retreat (see Fig. B2 and first block,
or minus 2.5 days. The MinAg observations are also pro- Table 1) and wheat harvest tends to occur approximately
vided in days of year with a range that varies from plus or 90–101daysbeforemonsoononset(seeFig.B3andsecond
minus 15 days depending on the location. Figure 8 shows block Table 1). These values (given in Table 1) provide the
therangeoftheMinAgsowingandharvestobservationsfor RelMonsoon values introduced in Sect. 2.3.1 used to
croprule
eachstate;thefullsowingorharvestwindowisshownbythe adjust the monsoon statistics and calculate the new sowing
downwardgreytriangles,withthemidpointsshownbyblack andharvestdatesbasedonthemonsoon.Therearesmallre-
trianglesjoinedbyablackline.Figure6considersthemid- gions with different monsoon characteristics and therefore
points of these two ranges in order to summarize how well muchearliersowingdays,forexampleforricesowinginthe
alignedthemonsoononsetrangeistotheobservedrangeof southern and far north of India. These regions have a direct
rice sowing dates, i.e. how the 5-day onset windows coin- impactonthevalues(minimum,maximum,mean,andstan-
cidewiththeobservedsowingwindow.Ifthemonsoononset darddeviation)giveninTable2,whichareaveragesforthe
rangeiscompletelywithintherangeofsowingdaysprovided whole of India and are discussed in more detail in Sect. 4.
by the observations, this is classed as a “hit” (shown by the Figure1highlightsthefactthattheaveragesowingandhar-
blueregions).Ifthemonsoononsetrangeiscompletelyout- vest dates for rice and wheat are closely aligned with the
side the range of observed sowing days, this is classed as monsoonprecipitationfromallthreeRCMsimulations.
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