Research Article La Niña Impacts on Austral …downloads.hindawi.com/journals/amete/2013/461693.pdfFazenda Santa Maria Fazenda Santa Maria 100102030 60 0 60 120 180 240 Gauge station

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2013 Article ID 461693 6 pageshttpdxdoiorg1011552013461693

Research ArticleLa Nintildea Impacts on Austral Summer ExtremelyHigh-Streamflow Events of the Paranaiacuteba River in Brazil

Netrananda Sahu1 RB Singh1 Pankaj Kumar1

Roberto Valmir Da Silva2 and Swadhin K Behera3

1 Department of Geography Delhi School of Economics University of Delhi Delhi 110007 India2 Environmental Engineering Federal University of Fronteira Sul Erechim 181 Brazil3 Research Institute for Global Change JAMSTEC Yokohama 236-0001 Japan

Correspondence should be addressed to Netrananda Sahu babunsahugmailcom and RB Singh rbsgeohotmailcom

Received 15 August 2013 Accepted 18 October 2013

Academic Editor Xiangzheng Deng

Copyright copy 2013 Netrananda Sahu et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The extremely high-streamflow events of the Paranaıba River basin are found to be associated with La Nina phenomenon duringDecemberndashFebruary (DJF) Extreme events are identified based on their persistent flow for seven days and more after takingretention time into consideration The extremely high-streamflow events are associated with the La Nina years 80 of the high-streamflow events have occurred during La Nina phases Therefore a very-significant 80 and above correspondence of theLa Nina events and the seasonal streamflow anomalies are found in DJF Although climate variations have direct relationshipwith the rainfall streamflow variations are considered as the surrogates to rainfalls However apart from climate variations theanthropogenic and land-use changes also influence streamflow variations In this study we have appliedmultivelocity TOPMODELapproach and residual trend analysis to examine the impact of land-use to the streamflow at the Fazenda SantaMaria gauge stationsHowever the model residual trend analysis of the TOPMODEL approach cannot quantify the extent of land-use impact Thus LaNina phase is important components to understand and predict the streamflow variations in the Paranaıba River basin

1 Introduction

Streamflow plays a major role in the livelihood of the peoplein a river catchment Hence the scientific analysis of stream-flow is very essential for the present and future generationsThe influences of climate variability on the streamflowshave been studied by Sahu et al [1ndash3] in their previousstudies of Indonesia and found very good correlation ofthe impact of climate variability on the streamflow Severalstudies performed on southeastern South America have usedstreamflows as indicators of climatic variability from theinterannual to the seasonal scale [4ndash6] It is stated that theclimate variability and changes can be studied by analyzingriver flows as a surrogate to rainfall under the assumptionthat changes in the rainfall are reflected and likely amplifiedin streamflows [7 8] Moreover it is easier to detect a changein streamflow than to directly observe changes in the basicclimatic variables [9]

The Paranaıba River flows in the Rio Paranaıba of Braziland in the state of Minas Gerais of the Mata da CordaMountains (19∘1310158402110158401015840S and 46∘1010158402810158401015840W)The river is flowingat an altitude of 1148 meters The length of the riveris approximately 1000 kilometers The Paranaıba and theGrande River both confluence and then form the secondlargest Parana River of Brazil at the point tomake the bordersbetween the states of Sao Paulo and Minas Gerais [10] Thecatchment area of the Paranaıba is approximately 36000 km2However Fazenda Santa Maria gauge station (17∘5810158405110158401015840Sand 50∘1410158404910158401015840W Figure 1) is in the Upper Paranaıba Rivercatchment having a catchment area of about 16750 km2 TheUpper catchment is not artificially regulated thus it is bestsuited for our analysis to minimize anthropogenic influenceson streamflow [1] This river is the primary source of waterto the Parana River The water resources of this basin sustainone of the most densely populated regions of South America

2 Advances in Meteorology

Paran

aiba R

iver

Paranaiba River

17∘58

99840051

998400998400S and 50∘14

99840049

998400998400W

48∘09984000998400998400W50

∘09984000998400998400W52

∘09984000998400998400W

48∘09984000998400998400W

W

N

E

50∘09984000998400998400W52

∘09984000998400998400W

16∘09984000998400998400S

18∘09984000998400998400S

20∘09984000998400998400S

16∘09984000998400998400S

S

18∘09984000998400998400S

20∘09984000998400998400S

(km)

(km)

Water body

Fazenda Santa Maria

Fazenda Santa Maria

10 0 10 20 30

60 0 60 120 180 240

Gauge station

River

Figure 1 The Paranaiba River basin with Fazenda Santa Maria (green mark) gauge station

where harvests and livestock are among the regionrsquos mostimportant assets [10]

The physical characteristics of a river basin and the rela-tionship between the climatic behavior of rainfall and itshydrologic response through streamflow can present dif-ferent degrees of complexity [1 11 12] Streamflow is asynthesis of precipitation and evapotranspiration and variouscomponents of the hydrologic cycle together with possi-ble anthropogenic influences [13] The rainfall variation inNortheast Brazil is shown to be influenced by variability inthe tropical Atlantic besides El NinoLa Nina [14 15] Inthis study we investigate the ENSO (El Nino and SouthernOscillation) particularly La Nina relationship at the basinscale The signature of La Nina is found in the extremelyhigh discharge events of DecemberndashFebruary (DJF) in theParanaiba River basin This paper also applies the hydrologi-cal model TOPMODEL [16] with a multivelocity approach inorder to investigate the land-use change on discharges

2 Data and Methods

21 Model Input Data The topographic data used in thisstudy were extracted by using ETOPO1 elevations global datafrom National Geophysical Data Center (NGDC) National

Oceanic andAtmospheric Administration (NOAA)The top-ographic data were composed of basin boundary slopescells distances (distance to the next downward cell) cellsareas and cumulative areas Precipitation data were obtainedfrom ANA (Brazilian National Agency of Water Resources)in two stations Fazenda Alianca and Maurilandia Meteo-rological data (radiation and temperature) were extractedfrom Hirabayashi et alrsquos [17] reanalysis They developed andassessed a global 05 degree near-surface atmospheric datafrom 1948 to 2006 at daily time scale we used data from 1978ndash2006

Potential evapotranspiration was estimated through thePriestley-Taylor radiation method [18] As TOPMODEL isa lumped hydrological model an aerial average daily pre-cipitation (Figure 2) and evapotranspiration (Figure 3) datawere used as input For this period (1978ndash2006) the meanprecipitation value was 394mm with a maximum value of10895mm whereas the mean evapotranspiration value was411mm with a maximum value of 637mm and minimumvalue of 234mm Daily discharges data were acquired fromANA at Fazenda Santa Maria station They encompass theperiod from 1978 to 2006 The last six years (2001ndash2006) ofthis time series were used for model calibration purpose andthe entire time series was used for model validation purpose

Advances in Meteorology 3

0 2000 4000 6000 8000 10000 120000

20

40

60

80

100

120

Time (d)

Precipitation Linear

Prec

ipita

tion

(mm

d)

Figure 2 Areal daily precipitation from 1978 to 2006

0 2000 4000 6000 8000 10000 120002

253

354

455

556

657

Time (d)

EvapotranspirationLinear

Evap

otra

nspi

ratio

n (m

md

)

Figure 3 Areal daily evapotranspiration calculated with thePriestley-Taylor method from 1978 to 2006

22 Climatology and Composite Index Data Daily climatol-ogy and anomalies of river discharge are computed from the29-year data Extremely high discharge events were catalogedbased on a threshold 15120590 (120590 stands for standard deviation)was set as threshold for extremely high discharges eventsThe NCEPNCAR (National Centers for Environmental Pre-dictionNational Center for Atmospheric Research) globalatmospheric reanalysis-1 zonal wind (850 hPa) dataset [19] isused from January 1 1979 to December 31 2008 The othermajor dataset used in this study is the global coverage NOAAinterpolated of daily averages of outgoing longwave radiationanomalies (here after OLR) data on a 25∘ times 25∘ grid atstandard pressure levels from 1 January 1979 to 31 December2008 [20] In addition to these the SST anomalies are usedfrom the daily OISST analysis version 2 AVHRR-AMSR(Advanced Very High Resolution Radiometer-AdvancedMicrowave Scanning Radiometer) products from NationalClimate Data Center (NCDC) from 1981 to 2008 [21]

3 Paranaiacuteba Streamflow Characteristics

The climatology of streamflow (Figure 4(a)) at the FazendaSanta Maria gauge station of the Paranaıba River in Brazilshows significant flow from November to May and very littleflow from June to October The variation in this seasonalstreamflow significantly affects the human population [10]A linear trend is seen in the streamflow at the Santa Mariastations During the season we have found that the El NinoModoki influence reduces the streamflow to nearly half ofthe average streamflow of the whole time series for extremelylow-discharge events [3] However in this study we haveinvestigated the influences of La Nina for extremely high-streamflow events (Figure 4(b))

It is important to understand the underlyingmechanismsthat cause the variation of streamflows due to the influencesof La Nina on the Paranaıba streamflows A scientific analysisis made to link the streamflow variability with the rainfalland SST and OLR variations on daily time scale like theprevious studies [1 2] Apart from the climate variabilityimpact in this study we have applied multivelocity approachTOPMODEL to examine the land-use influences on thestreamflow because the river streamflows unlike the rainfallare affected by morphological and anthropogenic factorsincluding soil and forestry recharge sediment deposit topog-raphy and land-use changes

4 Hydrological Model Approach

The multivelocity model approach which is consistent withfield observations carried out by Leopold et al [22] consistsin deriving a time-area function from a distance-area func-tion using the following equation

tc119896=

119873

sum

119896=1

119897119896

1198811015840

119862119867

1198601198811015840

119877

119870

(1)

where tc119896(T) is the time of concentration of a determined

distance-area function class 119896 1198811015840119862119867

is a proportionalityconstant (L-1T-1) 1198811015840

119877

is a power law exponent (ndash) 119897119896is

the plan flow path length from a class area 119896 to the basinoutlet 119860

119870(L2) is the cumulative area of the class 119896 and

119873 is the total number of classes which the distance-areafunction is composed Details about this approach and itsimplementationmay be seen in thework of Silva et al [23 24]

In order to evaluate the model performance Nash coeffi-cient [25] and log Nash coefficient were chosen as follows

NSE (Θ) = 1 minussum119873

119905=1

(119900 (119905) minus 119900 (119905 | Θ))2

sum119873

119905=1

(119900 (119905) minus 119900)2

NSElog (Θ) = 1 minussum119873

119905=1

(ln (119900 (119905)) minus ln (119900 (119905 | Θ)))2

sum119873

119905=1

(ln (119900 (119905)) minus ln (119900))2

(2)

where 119900(119905) is the observed discharge at the time 119905 119900(119905 | Θ)is the calculated discharge at the time 119905 given the parameterset Θ 119900 is the observed discharge average and 119873 is thenumber of time steps Thereby the model performance (Em)


450

400

350

300

250

200

150

100

50

0

D J F M A M J J A S O NMonths

Disc

harg

e (m

3s

)

(a)

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

1400

1200

1000

800

600

400

200

0

Disc

harg

e (m

3s

)

(b)

Figure 4 (a) Streamflow Climatology at Fazenda Santa Maria gauge station from 1978 to 2006 (b) Extremely high-streamflow events as perTable 1 during DJF seasons

Table 1 Extreme high river discharge events together with theclimate conditions during those events mLa Nina correspond to LaNina Modoki respectively

Extremely high dischargeevents

Average daily streamflows(m3s)number of days

DecemberndashFebruary1981-82 (La Nina) 58771981-82 (La Nina) 457311981-82 (La Nina) 871231984-85 (mLa Nina) 704101989-90 (La Nina) 604141989-90 (La Nina) 570112001-02 (La Nina) 579102001-02 (La Nina) 57381993-94lowast 58614lowast

refers to ldquonormal yearrdquo without any influence of La Nina

is determined by the product of these two coefficients that isby the product of (1) and (2) This is an attempt to search forsimulations that try to fit the observed discharge data at highand low discharges simultaneously

The methodology consists basically of (1)model calibra-tion against a period of six years (2) model validation overthirty-one years and (3) model residual trend analysis

41 Model Performance In the calibration period the modelobtained a performance coefficient Em of 054 (6 years)and in the validation period Em was equal to 032 FromFigure 6 it is possible to see that most observed discharges layinside the uncertainty bounds of 90 and inside themaxmininterval Therefore the model was validated for the entiretime series The model residuals analysis (Figure 5) does notprovide a clear upward trend in the discharges This meansthat there may be very little difference between observedand calculated discharge increased along the time Howevera statistical test was carried out to find the significance ofthe trend on model residual Kruskal-Wallis test [26] wasapplied to identify significant difference among the first sixyears and the last six years (Figure 6) The test showed littledifference between the groups (group 1 and group 2 Figure 7)

times108

Disc

harg

e (m

3d

)

0 2000 4000 6000 8000 100000

05

1

15

2

25

Time (d)

Minmax limits90 uncertainty limitsObserved dischargeCalculated total dischargeCalculated subsurface discharge

Figure 5 Model calibration (Em = 054) and validation (Em =031) Period at right from the red dashed line was used for modelcalibration (2001ndash2006)The entire period (1978ndash2006) was used formodel validation

at119875 lt 005 It is probably due to the flux in the form of heat ormass transfers Nevertheless the land-use does not have verysignificant influences on the streamflow characteristics

5 Impact of La Nintildea on Austral Summer

To examine the possible other component impacts on stream-flow of the Paranaıba River we investigate the climate var-iability influences on the streamflow at Fazenda Santa Mariagauge station In this study we found that the La Nina hassignificant influence on Paranaıba streamflow during australsummer (DJF) As shown in Table 1 7 out of the total 9extremely low-discharge events are associated with La Ninaduring the austral summer season

Moreover 80 of extremely high discharge events arefound in the La Nina phase of austral summer (Table 1) Outof the 9 extremely high discharge events during the austral


0 2000 4000 6000 8000 10000Time (d)

ResidualLinear

times108

1

05

0

minus05

minus1

minus15

) di

scha

rge (

m3d

)(O

bsminus

calc

Figure 6 Model residual and difference between observed dis-charge and calculated discharge Data period from 1978 to 2006

1 2Group

times107

8

6

4

2

0

minus2

minus4

minus6

minus8

minus10

minus12

) di

scha

rge (

m3d

)(O

bsminus

calc

Figure 7 Frequency distribution of the first six years of modelresidual (group 1) and the last six years (group 2)

summer season 7 events are associated with La Nina andonly one event is associated with La Nina Modoki Thecomposite anomalies of SST wind and OLR for all the eventsduring the DJF extremely high streamflow depict a La Ninacondition when the eastern Pacific is colder than normal(Figure 8) Unlike the El NinoModoki related extremely low-streamflow events (figure not shown) we find here that thetropospheric subsidence associated with La Nina conditionis more confined to Amazon basin

We also notice anomalously strong winds blowing fromtropical Atlantic to most parts of Northeast Brazil includingthe Paranaıba catchment thereby introducing more surfacemoistures over that region This also explains the nega-tive OLR anomalies seen above that region and associatedextremely high streamflows Further velocity potential at200 hPa shows significant convergence over the Paranaıbacatchment (Figure 9) If we take the probability of occur-rences because of La Nina La Nina influences around 80of the extremely high discharge events

30N20N10NEQ10S20S30S40S50S

150E 180 150W 120W 90W 60W 30W 0

4

09

06

03

minus03

minus06

minus09

Figure 8 Composite anomalies of SST (shaded) wind (streamarrow) andOLR (contour) duringDJF orAustral summer season forall extremely high-streamflow events associated with La Nina Unitfor SST is ∘C for wind is m sminus1 and for OLR is wm2 Values above95 confidence level from a two-tailed Studentrsquos 119905 test are shown

20N10NEQ10S20S30S40S50S60S

150E 180 150W 120W 90W 60W 30W

09

05

06

04

minus07

minus06

minus04

minus08

minus09

09

09

05

05

06

06

04

04

04

04

minus07minus06

minus04

minus08minus08

minus09

minus09

Figure 9 Composite anomalies of 200 hPa velocity potentialanomalies (times106m2 sminus1 shaded) shaded values are significant at 90using t-test for DJF or Austral summer season for all extreme high-streamflow events associated with La Nina

If we compare these analyses with the multivelocity TOP-MODEL output we may conclude that climate variabilitysuch as La Nina influences the extremely high dischargesevents more than any other factor in the Paranaiba catch-ment as it is a general acceptance that land-use influencedmore to the high discharge events due to soil erosion sed-iment deposits and other anthropogenic land-use changesHere we recognize that climate modes could cause equal ormore amounts of damages to the streamflows

6 Conclusions

In this study we analyzed the daily streamflow of the Para-naıba River at the Fazenda Santa Maria gauge station oninvestigate the impact of climate variations Also we examinethe land-use influences to the streamflow by applying themultivelocity TOPMODEL approach by the residual analysisDuring DJF or austral summer season we found that 80 ofthe extremely high discharge events occurred when easternPacific represents a La Nina-like situation

The La Nina has significantly influenced the extremelyhigh-streamflow characteristic of the Paranaıba River Uppercatchment However the model residual trend analysis of theTOPMODEL approach cannot quantify the extent of land-use impact which implies that rainy seasonrsquos extremely highdischarge events of the Paranaıba River catchment at theFazenda Santa Maria gauge stations are influenced mostly


by the La Nina phases of the Pacific Hence for the societalbenefits of this densely populated region climate factorsshould be investigated properly with special references to theLa Nina phase of the Pacific

Acknowledgments

NCEPNCARreanalysis and ANA (Brazilian National Agen-cy ofWater Resources) andOISST analysis version 2AVHRR-AMSR (Advanced Very High Resolution Radiometer-Ad-vanced Microwave Scanning Radiometer) products fromNCDC (National Climate Data Center) are provided byNOAA (available online) USA

References

[1] N Sahu S K Behera Y Yamashiki K Takara and T YamagataldquoIOD and ENSO impacts on the extreme stream-flows ofCitarum river in Indonesiardquo Climate Dynamics vol 39 no 7-8 pp 1673ndash1680 2012

[2] N Sahu Y Yamashiki S Behera K Takara and T YamagataldquoLarge impacts of indo-pacific climate modes on the extremestreamflows of citarum river in indonesiardquo Journal of GlobalEnvironment Engineering vol 17 pp 1ndash8 2012

[3] N Sahu S K Behera J V Ratnam et al ldquoEl Nino Modokiconnection to extremely-low streamflow of the Paranaiba Riverin Brazilrdquo Climate Dynamics 2013

[4] S Hastenrath ldquoDiagnostic and prediction of anomalous riverdischarges in northern South Americardquo Journal of Climate vol3 pp 1080ndash1096 1990

[5] C R Mechoso and G P Iribarren ldquoStreamflow in SoutheasternAmerica and the Southern oscillationrdquo Journal of Climate vol5 no 12 pp 1535ndash1539 1992

[6] J L Genta G Perez-Iribarren and C R Mechoso ldquoA recentincreasing trend in the streamflow of rivers in southeasternSouthAmericardquo Journal of Climate vol 11 no 11 pp 2858ndash28621998

[7] F H S Chiew and T A McMahon ldquoDetection of trend orchange in annual flow of Australian riversrdquo International Jour-nal of Climatology vol 13 no 6 pp 643ndash653 1993

[8] A W Robertson and C R Mechoso ldquoInterannual and decadalcycles in river flows of southeastern South Americardquo Journal ofClimate vol 11 no 10 pp 2570ndash2581 1998

[9] J E Richey C Nobre and C Deser ldquoAmazon River dischargeand climate variability 1903 to 1985rdquo Science vol 246 no 4926pp 101ndash103 1989

[10] IGAM (Institute of Water Management of Minas Gerais)ldquoSurface water quality monitoring in the Paranaiba river basinduring 2007rdquo Annual Report IGAM 2008 Portuguese

[11] P Aceituno ldquoOn the fluctioning of the Southern oscillation inthe SouthAmerica sectorrdquoMonthlyWeather Review vol 116 no3 pp 505ndash524 1988

[12] J A Marengo ldquoVariations and change in South Americanstreamflowrdquo Climatic Change vol 31 no 1 pp 99ndash117 1995

[13] N O Garcıa and W M Vargas ldquoThe temporal climatic varia-bility in the rsquoRio de la Platarsquo basin displayed by the river dis-chargesrdquo Climatic Change vol 38 no 3 pp 359ndash379 1998

[14] R V Andreoli andM T Kayano ldquoENSO-related rainfall anom-alies in South America and associated circulation featuresduring warm and cold Pacific decadal oscillation regimesrdquo

International Journal of Climatology vol 25 no 15 pp 2017ndash2030 2005

[15] M T Kayano and R V Andreoli ldquoRelations of South Americansummer rainfall interannual variations with the Pacific DecadalOscillationrdquo International Journal of Climatology vol 27 no 4pp 531ndash540 2007

[16] K J Beven R Lamb P Quinn R Romanowicz and J FreerldquoTopmodelrdquo in Computer Models of Watersh V P Singh Edpp 627ndash668 Water Resources Publication 1995

[17] Y Hirabayashi S Kanae K Motoya K Masuda and P DollldquoA 59-year (1948ndash2006) global near-surfacemeteorological dataset for land surface modelsrdquo Development of Daily Forcingand Assessment of Precipitation Intensity Hydrological ResearchLetters vol 2 pp 36ndash40 2008

[18] C H B Priestley and R J Taylor ldquoOn the assessment of sur-face heat flux and evaporation using large-scale parametersrdquoMonthly Weather Review vol 100 no 2 pp 81ndash92 1972

[19] E Kalnay M Kanamitsu R Kistler et al ldquoThe NCEPNCAR40-year reanalysis projectrdquo Bulletin of the AmericanMeteorolog-ical Society vol 77 no 3 pp 437ndash471 1996

[20] B Liebman and C A Smith ldquoDescription of a complete (Inter-polated) outgoing longwave radiation datasetrdquo Bulletin of theAmerican Meteorological Society vol 77 pp 1275ndash1277 1996

[21] R W Reynolds T M Smith C Liu D B Chelton K S Caseyand M G Schlax ldquoDaily high-resolution-blended analyses forsea surface temperaturerdquo Journal of Climate vol 20 no 22 pp5473ndash5496 2007

[22] L B Leopold M G Wolman and J P Miller Fluvial Processesin Geomorphology Dover Publications 1964

[23] RV Silva Y Yamashiki K Tatsumi andK Takara ldquoLarge-scalerunoff routingmodeling using TOPMODELrdquoAnnual Journal ofHydraulic Engineering vol 54 pp 91ndash96 2010

[24] R V Silva F Grison and M Kobiyama ldquoConceptual inves-tigation of time of concentration Case study of the PequenoRiver watershed Sao Jose dos Pinhais PR Brazilrdquo in FromHeadwaters To the Ocean Taniguchi Ed Taylor amp FrancisGroup London UK 2009

[25] J E Nash and J V Sutcliffe ldquoRiver flow forecasting throughconceptual models part Imdasha discussion of principlesrdquo Journalof Hydrology vol 10 no 3 pp 282ndash290 1970

[26] M Hollander and D A Wolfe Nonparametric Statistical Meth-ods John Wiley amp Sons Hoboken NJ USA 1999

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Geological ResearchJournal of


Geology Advances in


Paran

aiba R

iver

Paranaiba River

17∘58

99840051

998400998400S and 50∘14

99840049

998400998400W

48∘09984000998400998400W50

∘09984000998400998400W52

∘09984000998400998400W

48∘09984000998400998400W

W

N

E

50∘09984000998400998400W52

∘09984000998400998400W

16∘09984000998400998400S

18∘09984000998400998400S

20∘09984000998400998400S

16∘09984000998400998400S

S

18∘09984000998400998400S

20∘09984000998400998400S

(km)

(km)

Water body

Fazenda Santa Maria

Fazenda Santa Maria

10 0 10 20 30

60 0 60 120 180 240

Gauge station

River

Figure 1 The Paranaiba River basin with Fazenda Santa Maria (green mark) gauge station

where harvests and livestock are among the regionrsquos mostimportant assets [10]

The physical characteristics of a river basin and the rela-tionship between the climatic behavior of rainfall and itshydrologic response through streamflow can present dif-ferent degrees of complexity [1 11 12] Streamflow is asynthesis of precipitation and evapotranspiration and variouscomponents of the hydrologic cycle together with possi-ble anthropogenic influences [13] The rainfall variation inNortheast Brazil is shown to be influenced by variability inthe tropical Atlantic besides El NinoLa Nina [14 15] Inthis study we investigate the ENSO (El Nino and SouthernOscillation) particularly La Nina relationship at the basinscale The signature of La Nina is found in the extremelyhigh discharge events of DecemberndashFebruary (DJF) in theParanaiba River basin This paper also applies the hydrologi-cal model TOPMODEL [16] with a multivelocity approach inorder to investigate the land-use change on discharges

2 Data and Methods

21 Model Input Data The topographic data used in thisstudy were extracted by using ETOPO1 elevations global datafrom National Geophysical Data Center (NGDC) National

Oceanic andAtmospheric Administration (NOAA)The top-ographic data were composed of basin boundary slopescells distances (distance to the next downward cell) cellsareas and cumulative areas Precipitation data were obtainedfrom ANA (Brazilian National Agency of Water Resources)in two stations Fazenda Alianca and Maurilandia Meteo-rological data (radiation and temperature) were extractedfrom Hirabayashi et alrsquos [17] reanalysis They developed andassessed a global 05 degree near-surface atmospheric datafrom 1948 to 2006 at daily time scale we used data from 1978ndash2006

Potential evapotranspiration was estimated through thePriestley-Taylor radiation method [18] As TOPMODEL isa lumped hydrological model an aerial average daily pre-cipitation (Figure 2) and evapotranspiration (Figure 3) datawere used as input For this period (1978ndash2006) the meanprecipitation value was 394mm with a maximum value of10895mm whereas the mean evapotranspiration value was411mm with a maximum value of 637mm and minimumvalue of 234mm Daily discharges data were acquired fromANA at Fazenda Santa Maria station They encompass theperiod from 1978 to 2006 The last six years (2001ndash2006) ofthis time series were used for model calibration purpose andthe entire time series was used for model validation purpose


0 2000 4000 6000 8000 10000 120000

20

40

60

80

100

120

Time (d)


Prec

ipita

tion

(mm

d)


0 2000 4000 6000 8000 10000 120002

253

354

455

556

657

Time (d)


Evap

otra

nspi

ratio

n (m

md

)








tc119896=

119873

sum

119896=1

119897119896

1198811015840

119862119867

1198601198811015840

119877

119870

(1)




119877







119905=1

(119900 (119905) minus 119900 (119905 | Θ))2

sum119873

119905=1

(119900 (119905) minus 119900)2


119905=1

(ln (119900 (119905)) minus ln (119900 (119905 | Θ)))2

sum119873

119905=1

(ln (119900 (119905)) minus ln (119900))2

(2)



450

400

350

300

250

200

150

100

50

0


Disc

harg

e (m

3s

)

(a)

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

1400

1200

1000

800

600

400

200

0

Disc

harg

e (m

3s

)

(b)










times108

Disc

harg

e (m

3d

)

0 2000 4000 6000 8000 100000

05

1

15

2

25

Time (d)








0 2000 4000 6000 8000 10000Time (d)

ResidualLinear

times108

1

05

0

minus05

minus1

minus15

) di

scha

rge (

m3d

)(O

bsminus

calc


1 2Group

times107

8

6

4

2

0

minus2

minus4

minus6

minus8

minus10

minus12

) di

scha

rge (

m3d

)(O

bsminus

calc




30N20N10NEQ10S20S30S40S50S

150E 180 150W 120W 90W 60W 30W 0

4

09

06

03

minus03

minus06

minus09


20N10NEQ10S20S30S40S50S60S

150E 180 150W 120W 90W 60W 30W

09

05

06

04

minus07

minus06

minus04

minus08

minus09

09

09

05

05

06

06

04

04

04

04

minus07minus06

minus04

minus08minus08

minus09

minus09



6 Conclusions





Acknowledgments


References





































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


0 2000 4000 6000 8000 10000 120000

20

40

60

80

100

120

Time (d)


Prec

ipita

tion

(mm

d)


0 2000 4000 6000 8000 10000 120002

253

354

455

556

657

Time (d)


Evap

otra

nspi

ratio

n (m

md

)








tc119896=

119873

sum

119896=1

119897119896

1198811015840

119862119867

1198601198811015840

119877

119870

(1)




119877







119905=1

(119900 (119905) minus 119900 (119905 | Θ))2

sum119873

119905=1

(119900 (119905) minus 119900)2


119905=1

(ln (119900 (119905)) minus ln (119900 (119905 | Θ)))2

sum119873

119905=1

(ln (119900 (119905)) minus ln (119900))2

(2)



450

400

350

300

250

200

150

100

50

0


Disc

harg

e (m

3s

)

(a)

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

1400

1200

1000

800

600

400

200

0

Disc

harg

e (m

3s

)

(b)










times108

Disc

harg

e (m

3d

)

0 2000 4000 6000 8000 100000

05

1

15

2

25

Time (d)








0 2000 4000 6000 8000 10000Time (d)

ResidualLinear

times108

1

05

0

minus05

minus1

minus15

) di

scha

rge (

m3d

)(O

bsminus

calc


1 2Group

times107

8

6

4

2

0

minus2

minus4

minus6

minus8

minus10

minus12

) di

scha

rge (

m3d

)(O

bsminus

calc




30N20N10NEQ10S20S30S40S50S

150E 180 150W 120W 90W 60W 30W 0

4

09

06

03

minus03

minus06

minus09


20N10NEQ10S20S30S40S50S60S

150E 180 150W 120W 90W 60W 30W

09

05

06

04

minus07

minus06

minus04

minus08

minus09

09

09

05

05

06

06

04

04

04

04

minus07minus06

minus04

minus08minus08

minus09

minus09



6 Conclusions





Acknowledgments


References





































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


450

400

350

300

250

200

150

100

50

0


Disc

harg

e (m

3s

)

(a)

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

1400

1200

1000

800

600

400

200

0

Disc

harg

e (m

3s

)

(b)










times108

Disc

harg

e (m

3d

)

0 2000 4000 6000 8000 100000

05

1

15

2

25

Time (d)








0 2000 4000 6000 8000 10000Time (d)

ResidualLinear

times108

1

05

0

minus05

minus1

minus15

) di

scha

rge (

m3d

)(O

bsminus

calc


1 2Group

times107

8

6

4

2

0

minus2

minus4

minus6

minus8

minus10

minus12

) di

scha

rge (

m3d

)(O

bsminus

calc




30N20N10NEQ10S20S30S40S50S

150E 180 150W 120W 90W 60W 30W 0

4

09

06

03

minus03

minus06

minus09


20N10NEQ10S20S30S40S50S60S

150E 180 150W 120W 90W 60W 30W

09

05

06

04

minus07

minus06

minus04

minus08

minus09

09

09

05

05

06

06

04

04

04

04

minus07minus06

minus04

minus08minus08

minus09

minus09



6 Conclusions





Acknowledgments


References





































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


0 2000 4000 6000 8000 10000Time (d)

ResidualLinear

times108

1

05

0

minus05

minus1

minus15

) di

scha

rge (

m3d

)(O

bsminus

calc


1 2Group

times107

8

6

4

2

0

minus2

minus4

minus6

minus8

minus10

minus12

) di

scha

rge (

m3d

)(O

bsminus

calc




30N20N10NEQ10S20S30S40S50S

150E 180 150W 120W 90W 60W 30W 0

4

09

06

03

minus03

minus06

minus09


20N10NEQ10S20S30S40S50S60S

150E 180 150W 120W 90W 60W 30W

09

05

06

04

minus07

minus06

minus04

minus08

minus09

09

09

05

05

06

06

04

04

04

04

minus07minus06

minus04

minus08minus08

minus09

minus09



6 Conclusions





Acknowledgments


References





































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in



Acknowledgments


References





































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in










Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

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Research Article La Niña Impacts on Austral …downloads.hindawi.com/journals/amete/2013/461693.pdfFazenda Santa Maria Fazenda Santa Maria 100102030 60 0 60 120 180 240 Gauge station