drought monitoring and mapping in indonesia under current and future climate conditions mamenun 1,...

Drought monitoring and mapping in Indonesia under current and future climate

conditions

Mamenun1, Ronald Vernimmen2

[email protected] , [email protected] [email protected]

BMKG

International workshop on the Digitation of Historical Climate Data, the new SACA&D Database and Climate Analysis in the Asian Region, Citeko 2-5 April 2012

1 Meteorological Climatological and Geophysiscal Agency of Indonesia (BMKG) Jl. Angkasa I No. 2 , Jakarta 10720 Indonesia

2 Deltares, P.O.Box 177. 2600 MH, Delft, the Netherlands

Outline

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Background

Joint Cooperation Program (JCP)

Drought Monitoring & Mapping

a. Using ground stations

b. Using sattellite observations

Validation TRMM satellite data withground stations

Monthly average

Monthly deficit precipitation

Next steps

Agroclimatic mapping

Drought occurance in the future

Summary

Background

Current status of the

climate

Droughts occur more frequently and more severe

what current condition as the result of what happened yesterday, and

will happen (forecast),

data is scattered, not available in real time, not easy accessible to stakeholders, lack of quality control, spatial distribution of ground stations

not always sufficientDevelopment of Early Warning System and Mapping for Drought

(DEWMS)

Impacts; failure of water systems (reservoirs, irrigation) which is effecting agriculture sector,

destructive infrastructure and environment, social economic and contribute to enhanced fire risk.

Some issues;

The needs;

To provide integrated system for Policymakers and stakeholders to make an assessment or strategy for water

resource, agriculture, environment, social economic

Joint Cooperation Programme

BMKG PusAir KNMI Deltares

Component A;General

Institutional Development

Component A;General

Institutional Development

Component B;Collborative

Development of Customized and

Standardized IWRM tools and

Approaches

Component B;Collborative

Development of Customized and

Standardized IWRM tools and

Approaches

Component C; Supporting the Development of

Consistent Datasets

Component C; Supporting the Development of

Consistent Datasets

Component D;Operational

Management Support: Drought and Flood Monitoring and

Warning

Component D;Operational

Management Support: Drought and Flood Monitoring and

Warning

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Joint Cooperation Programme

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JCP Framework

JCP Framework

Building DEWMS Indonesia

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Open Shell Forecasting System System for operational forecasting (resilience !) Fully configurable by users (Open Interface to models and data) Platform for operational research (Short cycle from research to operations) Java, PostgreSQL/Oracle, Jboss, XML Operating system independent, very scalable Toolbox for development of forecasting systems Highly modular structure – independent modules provide functionality Rapid implementation, scalable & flexible Automatic / manual & stand alone

System based on Delft-OMS = Delft-FEWS

http://publicwiki.deltares.nl/display/FEWSDOC/Home


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Delft-OMS• import

• validation

• transformation / interpolation

• data hierarchy

• general adapter

• export / report

• administration (data, forecasts)

• viewing (data, forecasts)

• archiving

• …

data feeds

models

export &

dessimination

PI

impo

rt

Concept


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As a pilot, for the Pemali Comal catchment in Central Java, manual ground measurements of rainfall serve as input for calculation of the Standardized Precipitation Index (SPI).

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Drought monitoring & mapping

A. Using Ground Stations


A key feature of the SPI is the flexibility to measure drought at different time scales.

Short term droughts of 1 month (SPI-01) defined by specific regional climatology;Agricultural important droughts over 3 to 6 months (SPI-03, SPI-06) resulting in deficits in soil moisture;Longer term droughts (months to years) have impact on surface and groundwater supplies.

The severity of a drought can be compared to the average condition for a particular station or region. Values range from 2.00 and above (extremely wet) to -2.00 and less (extremely dry) with near normal conditions ranging from 0.99 to -0.99.

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SPI value; Drought Category;

2.00 and above Extremely wet

1.50 to 1.99 Very wet

1.00 to 1.49 Moderately wet

-0.99 to 0.99 Near Normal

-1.00 to -1.49 Moderately dry

-1.50 to -1.99 Severely dry

-2.00 and less Extremely dry


Timeseries for individual stations are calculated

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SPI-12SPI-12SPI-6SPI-6

SPI-3 SPI-3 SPI-1 SPI-1

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(April 2007)


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Validation of TRMM 3B42RT (TMPA) satellite data with ground stations on monthly basisValidation of TRMM 3B42RT (TMPA) satellite data with ground stations on monthly basis

Location Rain gauge

TRMM Grid Cell

Jakarta 10 3

Bogor 10 4

Bandung 13 4

Banjarbaru 15 6

East Java 15 6

Lampung 13 5

Source : *Vernimmen, R. R. E., Hooijer, A., Mamenun, Aldrian, E., and van Dijk, A. I. J. M.: Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia, Hydrol. Earth Syst. Sci., 16, 133-146, doi:10.5194/hess-16-133-2012, 2012.

B. Using Satellite Observations


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Validation Region

Ground Stations

TMPA TMPA bias corr

P P Avg.diff

Rel. bias

RMSE R2P Avg.

diffRel. bias

RMSE R2

Jakarta 2010 1865 -145 -7.2 83.8 0.84 1918 -92 -4.6 78.2 0.84

Bogor 3056 2944 -112 -3.7 94.9 0.83 2845 -211 -6.9 79.8 0.84

Bandung 1723 1936 213 12.3 85.8 0.84 1965 242 14.0 71.6 0.86

East Java 2106 1835 -271 -12.8 56.0 0.95 1819 -287 -13.6 49.3 0.96

Banjar Baru 2208 2217 9 0.4 59.6 0.84 2303 95 4.3 56.0 0.85

Lampung 1946 2191 244 12.6 83.8 0.89 2200 254 13.1 63.6 0.90

Annual ground station and TMPA 3B42RT comparison before and after bias

correction of TMPA 3B42RT precipitation estimates over the period 2003–2008.

Correction Factor: TRMMCorr= 3.20 TRMM 0.79


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Validation Region

Ground Stations

TMPA TMPA bias corr

P P Avg.diff

Rel. bias

RMSE R2P Avg.

diffRel. bias

RMSE R2

Jakarta 319 276 -43 -13.5 50.5 0.62 340 21 6.6 51.2 0.65

Bogor 715 539 -176 -24.6 72.9 0.78 604 -111 -15.5 64.1 0.79

Bandung 286 204 -82 -28.7 33.9 0.87 265 -21 -7.3 29.7 0.87

East Java 166 75 -91 -55.1 31.8 0.91 114 -52 -31.3 23.6 0.92

Banjar Baru 462 467 5 1.0 36.0 0.85 551 89 19.3 40.2 0.85

Lampung 367 255 -121 -30.3 39.9 0.71 336 -8.4 -8.4 32.2 0.77

Dry season (June–October) ground station and TMPA 3B42RT comparison before and after

bias correction of TMPA 3B42RT precipitation estimates over the period 2003–2008.


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Validation resultValidation result


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Validation

result

Validation

result


TRMM satellite data are used for improved rainfall monitoring and assessing the current drought status.

TRMM 3B42RT satellite precipitation (in mm) over Indonesia on 28 March 2012 19:00 WIB.

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TRMM 3B42RT satellite precipitation aggregated to monthly totals are bias corrected using the method described in Vernimmen et al. 2012*, based on validation of TRMM 3B42RT with ground stations.

*Vernimmen, R. R. E., Hooijer, A., Mamenun, Aldrian, E., and van Dijk, A. I. J. M.: Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia, Hydrol. Earth Syst. Sci., 16, 133-146, doi:10.5194/hess-16-133-2012, 2012.

Bias corrected TRMM 3B42RT satellite precipitation (in mm) over Indonesia in March 2012.

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Climatology (monthly average) from corrected TRMM3B42RT

Monthly average on March 2012.

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Climatology of corrected monthly TRMM 3B42RT is used to calculate ‘Sifat Hujan’ (monthly rainfall compared to long-term average).

‘Sifat Hujan’ March 2012. Yellow is normal conditions, orange is drier while green is wetter compared to long-term average

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Monthly precipitation deficit is calculated. For evaporation, currently the CGIAR-PET* monthly dataset multiplied with a fixed crop factor of 0.8 is used.

Global CGIAR-PET is a modelled dataset (1 km resolution) using data available from WorldClim Global Climate Data over the period 1950-2000.

Precipitation deficit in March 2012. The precipitation deficit needs to be linked to drought indicators for different agricultural crops*http://www.cgiar-csi.org/data/item/51-global-aridity-and-pet-databaseBMKG


*http://www.cgiar-csi.org/data/item/51-global-aridity-and-pet-databaseBMKG

Deficit precipitation on watershed basin (DAS) for java location

March 2012


Using the TRMM 3B42RT satellite precipitation the following will also be implemented (in progress):

1. Onset of dry season, defined as 3 consecutive decadal (10-day) periods with precipitation < 50 mm

2. Similarly, onset of the wet season3. SPI4. Peat fire forecasting (through running a peatland water budget model; ground

water table depth is a better indicator for fire risk then precipitation alone)

Other suggestions?

ECMWF Seasonal Forecast data will be utilized in the near future as well.

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Next Steps :

Agroclimatic mapping using satellite observations

Oldeman agroclimatic maps for Indonesia based on corrected monthly TRMM satellite precipitation.

Classification based on number of wet and dry months in a year.

Wet month = long term average > 200 mm

Dry month = long term average < 100 mm

Historical maps (1980’s) based on ground stations measurements and used by ‘Pertanian’ (Ministry of Agriculture)

Oldeman, L. R., Las, I., and Darwis, S. N.: An agroclimatic map of Sumatra, Contributions, Central Research Institute for Agriculture, Bogor, No. 52, 35 pp., 1979.

Oldeman, L. R., Las, I., and Muladi: The agroclimatic maps of Kalimantan, Maluku, Irian Jaya and Bali, West and East Nusa Tenggara, Contributions, Central Research Institute for Agriculture, Bogor, No. 60, 32 pp., 1980.

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Oldeman classification

5 main zones

A has more than 9 consecutive wet months. Wetland rice can be cultivated any time of the year.

B has 7-9 consecutive wet months. Two wetland rice crops can be cultivated during this period.

C has 5-6 consecutive wet months. Two rice crops can be cultivated only, if the first rice crop is planted (or sown) as a dry land crop (so-called gogorancah system).

D has 3-4 consecutive wet months. Only one wetland rice crop is generally possible.

E has less than 3 consecutive wet months. Without additional water from irrigation, wetland rice is not recommended.

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Oldeman classification

These 5 main zones are subdivided based on length of dry season

1 less than 2 dry months. No restrictions are expected with regard to available water.

2 2-3 dry months. Careful planning is needed to grow crops throughout the year.

3 4-6 dry months. A fallow period is part of the rotation system because of water constraints.

4 7-9 dry months. Only one crop can successfully be cultivated. The remainder of the year is too dry.

5 more than 9 consecutive dry months. Areas in this subzone are generally not suitable for any cultivation of arable crops.

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Oldeman map Indonesia

Oldeman agroclimatic map based on bias corrected monthlyTRMM 3B42RT (left) compared tohistorical map (right) for Kalimantan

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Oldeman map Indonesia

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Similarly, the Schmidt-Ferguson (1951) climatic map is generated.

Different definition of dry and wet month!dry: < 60 mm (whereas Oldeman < 100 mm)wet: > 100 mm (whereas Oldeman > 200 mm)

Schmidt-Ferguson Climatic map

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Precipitation datasets from different Global Circulation Models (GCMs) under different IPCC scenario’s will be processed using Delft-OMS and will be used to generate precipitation change, drought occurance, Oldeman maps, etc.

These maps will help create an understanding of future drought vulnerabilities (which areas in Indonesia are vulnerable to climate change?) and will prepare for climate proofing of agricultural and water supply systems.

The following GCM’s will be considered:

Drought occurrence in the future

Model Institute Country Acronym

BCM2.0 Bjerknes Centre for Climate Research Norway BCCR

CGCM3.1 Canadian Centre for Climate modelling and Analysis Canada CCCMA

CGCM2.3.2 Meteorological Research Institute Japan CGCM

CSIRO-Mk3.0 Commonwealth Scientific and Industrial Research Organisation

Australia CSIRO

ECHAM5 Max Planck Institute Germany ECHAM

ECHO-G Freie Universität Berlin Berlin ECHO

GFDLCM 2.0 Geophysical Fluid Dynamics Centre USA GFDL

GISS ER Goddard institute for Space Studies USA GISS

IPSL CM4 Institute Pierre Simon Laplace France IPSL

MIROC3.2 Center of Climate System Research Japan MIROC

NCAR PCMI National Center for Atmospheric Research USA NCAR

HadGEM2 Met Office’s Hadley Centre for Climate Prediction UK HADGEM

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Drought monitoring and mapping both using ground stations and validated sattellite observation has been made as part of the development of Drought Early Warning and Mapping System

The average monthly and characteristic of climatology (sifat hujan), deficit rainfall in Indonesia and java’s watershed basin is calculated based on the corrected satellite data

The correction sattellite data will be applied on calculating SPI index, decadal precipitation for wet and dry onset, peat fire forecasting, and climate type

ECMWF Seasonal Forecast data will be utilized in the near future as well.

The climate scenario will be applied to project the drought occurance in the future

Summary

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drought monitoring and mapping in indonesia under current and future climate conditions mamenun 1,...

Documents

data platform

flood monitoring

months spi

month spi

short term droughts

climate analysis

ground stationsb

near normal conditions