JOINT COOPERATION PROGRAMME

Transcription

1 JOINT COOPERATION PROGRAMME Component D1: Droughts Early Warning System Document D1.9 Various materials: PPPs, Pusair Colloquium 2011 and Workshops 2012 Project: Client: Water Mondiaal Partners for Water Royal Netherlands Embassy in Jakarta Period: January 2011 March 2013

2 Table of Contents 1. Current status of FEWS and DEWS November Pusair Colloquium - Evaluation and correction of TRMM data for DEWS Indonesia Didah workshop 2012 Drought monitoring and mapping in Indonesia CORDEX Conference Korea Abstract Climate change impact in Indonesia 45

3 Current Status of the Development of a Flood and Drought Early Warning System for Indonesia Mamenun, Leni N., M. A. Arif, Agus S.H., R.Vernimmen International Workshop of Climate Change and Extreme Climate Over the Maritime Continent November 22-23, 2011 Outline November 22, Background Joint Cooperation Program (JCP) Building the systems Based System (Delft-OMS software) Concept Progress Activities Development of Drought Early Warning System Validation of TRMM satellite data with ground stations Validation result Oldeman map Standard Precipitation Index Development of Flood Early Warning System Basic scheme of FEWS Transformation of AWS data Transformation of TRMM data to telemetry stations Import CCAM data Next Steps Summary

4 Background November 22, Current status of the climate The needs; Floods and droughts occur more frequently and more severe Impacts; failure of water systems (reservoirs, irrigation) which is effecting agriculture sector, destructive infrastructure and environment, social economic and contribute to enhanced fire risk (drought). Some issues; 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 sufficient Development of Early Warning System for flood and drought (FEWS & DEWS) To provide integrated system for Policymakers and stakeholders to make an assessment or strategy for water resource, agriculture, environment, social economic Background November 22, Early Warning System 1. Risk Knowledge systematic assessment of hazards and vulnerabilities, and mapping of their patterns and trends. 2. Monitoring & Warning Service accurate and timely forecasting of hazards using reliable, scientific methods and technologies. 3. Dissemination & Communication clear and timely distribution of warnings to all those at risk. 4. Response Capability national and local capacities and knowledge to act correctly when warnings are communicated.

5 Background A System of Systems 4 Awareness Sub-Systems Forecasting Sub-Systems Warning Sub-Systems Action Sub-Systems Risk Assessments Science/Research Monitoring Network IT Infrastructure Rainfall Prediction Drought Prediction Flood Prediction Simulation Secure Communications Broadcast Media Person-to-person etc Evacuation Insurance Emergency Reinforcement Resettlement etc Joint Cooperation Program November 22, PusAir KNMI Deltares Component A; General Institutional Development Component B; Collborative Development of Customized and Standardized IWRM tools and Approaches Component C; Supporting the Development of Consistent Datasets Component D; Operational Management Support: Drought and Flood Monitoring and Warning

6 Joint Cooperation Program November 22, JCP Framework Building the system November 22, System based on Delft-OMS 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

7 Building the system November 22, Concept Delft- OMS data feeds Delft-OMS import validation transformation / interpolation data hierarchy general adapter export / report administration (data, forecasts) viewing (data, forecasts) archiving PI models Note: PI = Published Interface export & dessimination Progress activities November 22, Weather Forecast : - CCAM Validation Real Time Data (Radar) Telemetri (AWS) Synop, WLS Satellite Data (TRMM) Validation Validation Validation Flood Early Warning System Drought Early Warning System Ministry of Public Works Ministry of Agriculture Integrated Flood Risk Management Integrated Water Resource Management End Users (e.g. Reservoir Manager) Existed Program (Flood and Drought Information using observation (synop/raingauge data) and statistical analyses (ARIMA) Drought Forecast : - ECMWF Validation Downscalling Downscalling Climate Scenario Legend Finished In Progress... Next steps

8 Development of DEWS November 22, Validation of TRMM satellite data with ground stations on monthly basis Location Raingauge TRMM Grid Cell Jakarta 10 3 Bogor 10 4 Bandung 13 4 Banjarbaru 15 6 East Java 15 6 Lampung 13 5 TRMMCorr= 3.2 TRMM 0.79 Dry season; R 2 = Annual ; R Source : Vernimmen, A. Hooijer, Mamenun, E. Aldrian ( Development of DEWS November 22, Validation result

9 Development of DEWS November 22, Validation result Development of DEWS November 22, Oldeman map (Classification) 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 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.

10 Development of DEWS November 22, Oldeman map (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. Main Type Wet months Sub type A >9 1 <2 B C D >6 E <3 Results in a total of 18 climatic zones (14 of which in Indonesia): Dry months Development of DEWS November 22, Oldeman map m/asia/lists/cid.htm

11 Development of DEWS November 22, Compared of Oldeman map Based on TRMM data Development of DEWS November 22, Oldeman map for Indonesia (based on corrected TRMM data)

12 Development of DEWS November 22, Standard Precipitation Index (SPI) The Standard Precipitation Index (SPI) was designed to enhance the detection of onset and monitoring of drought (McKee et al, 1993) The SPI is a simpler measure of drought than the Palmer Drought Severity Index (PDSI) and is based solely on the probability of precipitation for a given time period. A key feature of the SPI is the flexibility to measure drought at different time scales. SPI value; Drought Category; 2.00 and above Extremely wet 1.50 to 1.99 Very wet 1.00 to 1.49 Moderately wet to 0.99 Near Normal to Moderately dry to Severely dry and less Extremely dry The methods are described here; documentation\spi.pdf Development of DEWS November 22, SPI map on Pemali Comal River Basin- Central Java 1 month 3 month 6 month 12 month

13 Development of DEWS November 22, Daily and monthly rainfall forecast from ECMWF (May 1, 2011) Development of FEWS 21 Basic Scheme of FEWS Import data from external sources Run hydrological model to calculate discharge for catchments and PusAir PusAir Calculate catchment average precipitation based on rain gauge data Prepare input for hydrodynamic model based on hydrological models, gauging stations, tidal boundaries Combine precipitation sources (e.g. Rain gauge, Radar data, Meteorological Forecast models) Run hydrodynamic model to calculate water level for forecast points Create evaporation profiles for hydrological model Run error correction model to update output form hydrodynamic model

14 Development of FEWS Transformation AWS Data Read AWS-online data with xml programming for input FEWS from 22 Development of FEWS 23 Analyze AWS data and adding into FEWS system

15 Development of FEWS 24 Non Equidistant Transformation AWS Data Finish Equidistant (per minute with MV) in progress Fill Missing Value (MV) Subtract Aggregation Transform from Point to Grid Development of FEWS 25 Transformation TRMM to telemetry stations Transformation

16 Development of FEWS 26 Transformation TRMM to telemetry stations Development of FEWS 27 Import CCAM Data Evaporation (hourly) Precipitation (hourly)

17 Next Steps November 22, until 2015; Install server at (January) and PusAir (March) Disseminate early warning on website Operationalize seasonal forecast data (ECMWF) Feasibility studies (1) in medium range weather forecasts based on 10 years TRMM time series, (2) fire risk in peat land, (3) drought warning for specific crops, (4) forecasting reservoir storage and release. Investigate and make an assessment for climate change effects especially on water resources and agriculture in order to the increasingly floods and droughts events in Indonesia Develop Climate change scenarios based on existing climate models (seek cooperation with Australia, KNMI-the Netherlands, JMA-Japan) Configure the climate change scenarios into Delft-OMS software in order to be able to develop mitigation and adaptation strategies for the future Summary 29 The data needs for FEWS and DEWS development are meteorological/climatological and hydrological data (historical, real time and forecast) which will be integrated into one system Flood and Drought early warning systems are still in development within Joint Cooperation Program between, PusAir, KNMI and Deltares The system is developed based on Delft-OMS software and will be operational at and PusAir Further it is planned to investigate climate change effects using analysis of long-term historical data and by running climate scenarios using the Delft-OMS software in order to be able to develop mitigation and adaptation strategies for the future

18 Discussion mamenun@gmail.com, leni.nazarudin72@gmail.com, arif.abdullah@bmkg.go.id, goesploes@yahoo.com, Ronald.vernimmen@deltares.nl Terima kasih... Thank you... Dank je wel...

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28 Drought monitoring and mapping in Indonesia under current and future climate conditions Mamenun 1, Ronald Vernimmen 2 mamenun@bmkg.go.id, mamenun@gmail.com ronald.vernimmen@deltares.nl 1 Meteorological Climatological and Geophysiscal Agency of Indonesia () Jl. Angkasa I No. 2, Jakarta Indonesia 2 Deltares, P.O.Box MH, Delft, the Netherlands 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 Outline 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 Summary Next steps Agroclimatic mapping Drought occurance in the future

29 Background Current status of the climate Droughts occur more frequently and more severe The needs; 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; what current condition as the result of what happened yesterday, and will happen (forecast), Development of Early Warning System and Mapping for Drought (DEWMS) data is scattered, not available in real time, not easy accessible to stakeholders, lack of quality control, spatial distribution of ground stations not always sufficient To provide integrated system for Policymakers and stakeholders to make an assessment or strategy for water resource, agriculture, environment, social economic Joint Cooperation Programme PusAir KNMI Deltares Component A; General Institutional Development Component B; Collborative Development of Customized and Standardized IWRM tools and Approaches Component C; Supporting the Development of Consistent Datasets Component D; Operational Management Support: Drought and Flood Monitoring and Warning

30 Joint Cooperation Programme JCP Framework Building DEWMS Indonesia System based on Delft-OMS = Delft-FEWS 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

31 Building DEWMS Indonesia Concept data feeds Delft-OMS import validation transformation / interpolation data hierarchy general adapter export / report administration (data, forecasts) viewing (data, forecasts) archiving PI models export & dessimination Building DEWMS Indonesia

32 Drought monitoring & mapping A. Using Ground Stations 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). Drought monitoring & mapping 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 and less (extremely dry) with near normal conditions ranging from 0.99 to SPI value; Drought Category; 2.00 and above Extremely wet 1.50 to 1.99 Very wet 1.00 to 1.49 Moderately wet to 0.99 Near Normal to Moderately dry to Severely dry and less Extremely dry

33 Drought monitoring & mapping Timeseries for individual stations are calculated Drought monitoring & mapping SPI-1 SPI-3 SPI-6 SPI-12 (April 2007)

34 Drought monitoring & mapping B. Using Satellite Observations Validation of TRMM 3B42RT (TMPA) satellite data with ground stations on monthly basis Location Rain gauge Jakarta 10 3 Bogor 10 4 Bandung 13 4 Banjarbaru 15 6 East Java 15 6 Lampung 13 5 TRMM Grid Cell 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, , doi: /hess , Drought monitoring & mapping Correction Factor: TRMMCorr= 3.20 TRMM 0.79 Validation Region Ground Stations P P Avg. diff TMPA Rel. bias RMSE R 2 P Avg. diff TMPA bias corr Rel. bias RMSE R 2 Jakarta Bogor Bandung East Java Banjar Baru Lampung Annual ground station and TMPA 3B42RT comparison before and after bias correction of TMPA 3B42RT precipitation estimates over the period 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, , doi: /hess , 2012.

35 Drought monitoring & mapping Validation Region Ground Stations P P Avg. diff TMPA Rel. bias RMSE R 2 P Avg. diff TMPA bias corr Rel. bias RMSE R 2 Jakarta Bogor Bandung East Java Banjar Baru Lampung Dry season (June October) ground station and TMPA 3B42RT comparison before and after bias correction of TMPA 3B42RT precipitation estimates over the period 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, , doi: /hess , Drought monitoring & mapping Validation result 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, , doi: /hess , 2012.

36 Drought monitoring & mapping Validation result Drought monitoring & mapping 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 :00 WIB.

37 Drought monitoring & mapping TRMM 3B42RT satellite precipitation aggregated to monthly totals are bias corrected using the method described in Vernimmen et al *, based on validation of TRMM 3B42RT with ground stations. Bias corrected TRMM 3B42RT satellite precipitation (in mm) over Indonesia in March * 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, , doi: /hess , Drought monitoring & mapping Climatology (monthly average) from corrected TRMM3B42RT Monthly average on March 2012.

38 Drought monitoring & mapping Hujan -term average). Sifat Sifat Hujan compared to long-term average Drought monitoring & mapping 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 Precipitation deficit in March The precipitation deficit needs to be linked to drought indicators for different agricultural crops *

39 Drought monitoring & mapping Deficit precipitation on watershed basin (DAS) for java location March 2012 * Drought monitoring & mapping Next Steps : 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 season 3. SPI 4. 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.

40 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 used by (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., 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., 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.

41 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 dry months. Careful planning is needed to grow crops throughout the year dry months. A fallow period is part of the rotation system because of water constraints 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. Oldeman map Indonesia Oldeman agroclimatic map based on bias corrected monthly TRMM 3B42RT (left) compared to historical map (right) for Kalimantan

42 Oldeman map Indonesia Schmidt-Ferguson Climatic map 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)

43 Drought occurrence in the future Precipitation datasets from different Global Circulation Models (GCMs) under -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. 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 UK HADGEM Summary 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 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

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45 Climate Impact Assessment in Indonesia Using Bias Corrected TRMM and Climate Scenario s from Different GCMs Mamenun 1, Budi Setiawan 1, Endar Pangestuti 1, Ronald Vernimmen 2 1 Center for Climate Change and Air Quality The Indonesian Agency for Meteorology, Climatology and Geophysics Jl. Angkasa 1 No. 2 Kemayoran, Central Jakarta, Indonesia 2 Deltares, P.O.Box MH, Delft, the Netherlands mamenun@bmkg.go.id, budbey@yahoo.com, endarpangestuti@gmail.com, ronald.vernimmen@deltares.nl ABSTRACT Indonesia is a tropical country and most parts of it receive abundant annual rainfall, in excess of 1800 mm per year. In many areas, however, rainfall is highly seasonal, and sometimes erratic. In such regions, prolonged water deficits lasting several months occasionally cause failures of water supply systems and of rain fed and irrigated crops, and frequently contribute to enhanced fire risk in forests and peat land areas. Monitoring and understanding dry season rainfall patterns, in time and space, is therefore important for the country to be prepared for water shortages, food shortages and fires. In order to tackle these issues a Drought Early Warning and Mapping System (DEWMS) for the whole of Indonesia is currently being developed using Delft-OMS software under a Joint Cooperation Program with, PusAir, KNMI and Deltares. The drought frequency and severity maps produced with this system are based on bias corrected TRMM satellite rainfall data. The bias correction was derived after a comparative analysis with ground station data in six regions. The corrected TRMM satellite rainfall data has been used to calculate climatology of rainfall, deficit water on river basin in Java Island and Oldeman agroclimatic maps for 3 provinces located on different islands. In addition to the development of DEWMS, Delft-OMS software is also used to prepare GCM data for Indonesia in order to be used in climate impact assessments. Currently, A1B IPCC scenario s from several GCM models; CSIRO, ECHAM, CCCMA and CGCM model, have been downscaled, bias corrected using the CRU-ERA40 dataset and stored in the system. The corrected GCM models have been used to project the rainfall, to assess rainfall climatology, and to build the Oldeman agroclimatic maps. For this future projection, it showed that the building of baseline datasets based on Indonesia s rain gauge are very needed to have a better projection in Indonesia. After an analysis of the drought conditions and associated vulnerabilities within Indonesia under the current climate, rainfall projections from the different GCMs will be analysed to determine the impact of the different climate scenario s. The maps and analysis will provide insight and understanding of drought patterns needed for many aspects of water and land management in Indonesia: not only drought preparedness but also climate proofing of agricultural and water supply systems for the future. Keywords: satellite rainfall, drought maps, GCM data, climate impact assessment.