meteorological drought monitoring based on satellite ... · sparse network of raingauges !high...

Motivation Objective TRMM Methodology Results Conclusions References

Meteorological drought monitoring basedon satellite rainfall estimates:

A case study on the Andean Rapel River Basin

(Chile)

“Coping with Droughts”, MWAR-LAC, Santiago, Chile

Mauricio Zambrano-Bigiarini

November 19th 2014Fac. de Ciencias Ambientales y Centro EULA-Chile

Universidad de Concepcion, Concepcion, Chile

mauricio.zambrano @ udec.cl

1 / 22SPI-TRMM 3B43.V7-MWAR-LAC 2014

Daily precipitation: spatial distribution

25 raingauges (DGA): ∼ 1 each 550 km2 (0-1500 msnm)61

000250000

250000

300000

350000

400000

LeyendaEst_Pluviometricas

Glaciar_Universidad

Glaciares

Rios_Principales

Esteros_Principales

Ciudades

SubCuencas_Rio_Rapel

Cuenca_Rio_Rapel10 0 10 20 30 40 km

Daily precipitation: little data at high elevations

∼ 31% of the area without observations (highest precipitation values)

Daily precipitation: ∼short records, with gaps

N° de días con información por año, en cada estación pluviométrica Cuenca del Río Rapel

P06003001−4P06006001−0P06008009−7P06010015−2P06012003−KP06013005−1P06015003−6P06016004−KP06018010−5P06019003−8P06019005−4P06027003−1P06028001−0P06034003−KP06036001−4P06040001−6P06042004−1P06044001−8P06055003−4P06056003−KP06120001−0P06130001−5P06130002−3P06130003−1P06132002−4

Issues for traditional drought monitoring:

Short or incomplete historical records → gap filling (from otherincomplete historical records).

Sparse network of raingauges → high uncertainties in spatialdistribution of precipitation.

Absence of measurements at high elevations → underestimationof precipitation amount in the headwaters.

Decision-making under uncertainty:

Difficulties for planning ahead and implementation of ad-hocmitigation policies.

Objective

To assess the suitability of the satellite-based TRMM 3B43.V7data as a free and publicly available precipitation data source formeteorological drought monitoring in data-scarce areas, inparticular in the Rapel River Basin in the central Chilean Andes(33◦51’ - 35◦01’ S).

Tropical Rainfall Measuring Mission (TRMM) Huffman et al. 2007

Quasi-global and near-real-time satellite rainfall estimates (SRE)(50◦N - 50◦S), from different satellite-borne sensors.

Spatial resolution: 0.25◦x 0.25◦

Temporal resolution: every 3 hrs (since 01-Jan-1998).

Available products: near-real-time (3B42RT), daily (3B42) andmonthly (3B43).

TRMM: Instruments

TRMM Microwave Imager (TMI):passive microwave sensor designed toprovide quantitative rainfall informationover a wide swath.

Precipitation Radar (PR): 3D maps ofstorm structure. Able to detect fairly lightrain rates (0.7 mm/hr).

Visible and InfraRed Scanner (VIRS):delineation of rainfall and communicationwith POES and GOES.

Cloud and Earth Radiant Energy Sensor(CERES).

Lightning Imaging Sensor (LIS).

TRMM: Instruments

Methodology

Assessment of TRMM 3B43.V7 SRFE against ground raingauges(DGA):

1 Download TRMM 3B43.V7 satellite data (Huffman et al., 2010).

2 Re-projection and application of zonal mask (33◦51’ - 35◦01’ S).

3 Point-to-pixel comparison: TRMM 3B43.V7 vs DGA (Thiemig et al., 2012).

SPI computations:

1 carried out with the SPEI R package (Beguerıa and Vicente-Serrano, 2014).

2 SPI computed for each grid cell at 1-, 3-, 6-, 9-, 12- and 24-monthtime scales.

Methodology

SPI computations:

Methodology

SPI computations:

Methodology

SPI computations:

Methodology

SPI computations:

1 Motivation

2 Objective

3 Tropical Rainfall Measuring Mission (TRMM)

4 Methodology

5 ResultsPoint-to-pixel comparisonsSPI: time seriesSPI: Spatial distribution

6 Conclusions

Estacion Coltauco

Jan2000

Jan2001

Jan2002

Jan2003

Jan2004

Jan2005

Jan2006

Jan2007

Jan2008

Jan2009

Jan2010

Jan2011

Dec2011

Estación P05: Coltauco Cod.BNA: 06012003−K. Altitud: 280 [m snm]

Tiempo

ón, [

] MedicionesTRMM.3B42_v7

Estacion San Fernando - DCP

Jan2000

Jan2001

Jan2002

Jan2003

Jan2004

Jan2005

Jan2006

Jan2007

Jan2008

Jan2009

Jan2010

Jan2011

Dec2011

Estación P08: San Fernando−DCP Cod.BNA: 06016004−K. Altitud: 350 [m snm]

Tiempo

ón, [

] MedicionesTRMM.3B42_v7

1 Motivation

2 Objective

4 Methodology

6 Conclusions

Jan1998

Jan1999

Jan2000

Jan2001

Jan2002

Jan2003

Jan2004

Jan2005

Jan2006

Jan2007

Jan2008

Jan2009

Jan2010

Jan2011

Jan2012

Jan2013

Jan2014

2SPI−12 at station P05. Nombre: Coltauco

Altitud: 280. Cod.BNA: 06012003−K. ID: P05

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● RaingaugesTRMM

Jan1998

Jan1999

Jan2000

Jan2001

Jan2002

Jan2003

Jan2004

Jan2005

Jan2006

Jan2007

Jan2008

Jan2009

Jan2010

Jan2011

Jan2012

Jan2013

Jan2014

SPI−12 at station P08. Nombre: San Fernando−DCP Altitud: 350. Cod.BNA: 06016004−K. ID: P08

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Jan1998

Jan1999

Jan2000

Jan2001

Jan2002

Jan2003

Jan2004

Jan2005

Jan2006

Jan2007

Jan2008

Jan2009

Jan2010

Jan2011

Jan2012

Jan2013

Jan2014

2SPI−12 at station P11. Nombre: Pichidegua Altitud: 125. Cod.BNA: 06019005−4. ID: P11

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Jan1998

Jan1999

Jan2000

Jan2001

Jan2002

Jan2003

Jan2004

Jan2005

Jan2006

Jan2007

Jan2008

Jan2009

Jan2010

Jan2011

Jan2012

Jan2013

Jan2014

SPI−12 at station P12. Nombre: La Rufina Altitud: 735. Cod.BNA: 06027003−1. ID: P12

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Jan1998

Jan1999

Jan2000

Jan2001

Jan2002

Jan2003

Jan2004

Jan2005

Jan2006

Jan2007

Jan2008

Jan2009

Jan2010

Jan2011

Jan2012

Jan2013

Jan2014

2SPI−24 at station P11. Nombre: Pichidegua Altitud: 125. Cod.BNA: 06019005−4. ID: P11

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Jan1998

Jan1999

Jan2000

Jan2001

Jan2002

Jan2003

Jan2004

Jan2005

Jan2006

Jan2007

Jan2008

Jan2009

Jan2010

Jan2011

Jan2012

Jan2013

Jan2014

SPI−24 at station P12. Nombre: La Rufina Altitud: 735. Cod.BNA: 06027003−1. ID: P12

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1 Motivation

2 Objective

4 Methodology

6 Conclusions

SPI−12 values based on TRMM 3B42v7 Rapel River Basin

6150000

6200000

6250000

Apr.2013 May.2013 Jun.2013 Jul.2013

Aug.2013 Sep.2013 Oct.2013 Nov.2013

6150000

6200000

6250000

Dec.2013 Jan.2014 Feb.2014 Mar.2014

250000300000350000400000

Apr.2014 May.2014

250000300000350000400000

Jun.2014 Jul.2014

−2.0

−1.5

−1.0

−0.5

SPI−24 values based on TRMM 3B42v7 Rapel River Basin

6150000

6200000

6250000

Apr.2013 May.2013 Jun.2013 Jul.2013

Aug.2013 Sep.2013 Oct.2013 Nov.2013

6150000

6200000

6250000

Dec.2013 Jan.2014 Feb.2014 Mar.2014

250000300000350000400000

Apr.2014 May.2014

250000300000350000400000

Jun.2014 Jul.2014

−1.5

−1.0

−0.5

SPI values based on TRMM 3B42v7 (Jul−2014) Rapel River Basin

6150000

6200000

6250000

SPI.1 SPI.3 SPI.6

250000 300000 350000 400000

SPI.9 SPI.12

250000 300000 350000 400000

SPI.24

−1.5

−1.0

−0.5

Conclusions

1 Monthly TRMM 3B43.V7 values correctly reproduced the shape of the observed signaland the amount of precipitation registered at 9 raingauges (DGA).

2 The validity of the aforementioned comparison is limited by the lack of observed dataabove 1500 m asl.

3 SPI analysis (12 and 24-month scales) correctly identified two severe meteorologicaldroughts: 1998/1999 and 2007/2008.

4 Negative values of SPI-12 and SPI-24 all over the basin (July 2014) indicate that adecreasing trend of precipitation is ongoing.

5 SPI values were lower in high elevation areas (no raingauges !) → likely severe negativeimpacts on agriculture, reservoirs and groundwater levels.

6 The proposed approach might be used as a cost-effective solution to supportdecision-making or as a first step towards and early warning system in data-scarse regionsin LAC.

7 Future improvements: SPEI instead of SPI ...

< mauricio.zambrano @ udec.cl >

Conclusions

Referencias

Beguerıa, S., Vicente-Serrano, S.M., 2014. SPEI: Calculation of theStandardised Precipitation-Evapotranspiration Index. URL:http://CRAN.R-project.org/package=SPEI. r package version 1.6.

Huffman, G.J., Adler, R.F., Bolvin, D.T., Nelkin, E.J., 2010. The TRMMmulti-satellite precipitation analysis (TMPA), in: Gebremichael, M., Hossain,F. (Eds.), Satellite Rainfall Applications for Surface Hydrology. SpringerDordrecht Heidelberg, London New York, pp. 3–22.doi:10.1007/978-90-481-2915-7_1.

R Core Team, 2013. R: A Language and Environment for StatisticalComputing. R Foundation for Statistical Computing. Vienna, Austria. URL:http://www.R-project.org/.

Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., De Roo, A., 2013.Hydrological evaluation of satellite-based rainfall estimates over the Voltaand Baro-Akobo Basin. Journal of Hydrology 499, 324–338.doi:10.1016/j.jhydrol.2013.07.012.

Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., Levizzani, V., De Roo, A.,2012. Validation of satellite-based precipitation products over sparselygauged African river basins. Journal of Hydrometeorology 13, 1760–1783.doi:10.1175/JHM-D-12-032.1.

meteorological drought monitoring based on satellite ... · sparse network of raingauges !high...

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