JOINT COOPERATION PROGRAMME

JOINT COOPERATION PROGRAMME Component D1: Droughts Early Warning System

Document D1.1 PPPs training on SPI and drought mapping

Bandung 11-13 May 2011

Project: 1201430.000 Client: Water Mondiaal Partners for Water Royal Netherlands Embassy in Jakarta Period: January 2011 – March 2013

Table of Contents 1. 2. 3. 4. 5. 6.

Training programme DEWMS exercises SPI and Theory of run [Indonesian] Drought mapping and implementation on DEWMS Software pengolahan data hidrologi [ Indonesian] Presentation pelatihan [Indonesian]

3 5 44 53 65 67

Program 11-13 May 2011

Ronald Vernimmen

Program

Wednesday 11 May 2011

Delft-OMS training (summary last training, new updates, new training subjects) Distribution of update DEWMS + latest TRMM data Notes with update DEWMS: added additional columns: DATE_ADDED / ADDED_BY / DISCHARGE / WATERLEVEL / UPSTREAM_AREA in metadata file missing values in Data Editor: use NaN, not -999! new feature: open street map layer / double mass curve

Program

Thursday 12 May 2011

Discussion: projects staff is working on now and the next 3-6 months (fill in Excel sheet) planning, staff availability for the next 3-6 months (fill in Excel sheet) schedule workshops / training, new ones, fixed dates for scheduled ones (check planning Excel sheet) tasks for the next visit (Pemali Comal filling database) Demonstration HITA

TIDEDA

NeoPERDAS (Ibu Henny)

Drought presentations (Ibu Lanny, Ronald) Continued Delft-OMS training

Program

Friday 13 May 2011

Delft-OMS training based on existing configuration course November 2010 (Island of Man, England)

Distribute of final DEWMS (incl. database with Open Street Map layers) updates: (1) changes to metadata file (adding LS and BT) (2) icon file meteo (3) changed parameter names H.m.transformed / P.m.transformed)

Distribute Fews configuration course Distribute presentations + background documentation (incl. Hita and NeoPerdas software PusAir) WinRAR (program to open the zipped DEWMS file) Editix (XML editor) Logbook data editing (Logbook_data_editing_DEWMS.xls) Questions: 1. who has digital copies of WMO guidelines? 2. who has experience with Matlab?

DEWMS exercises

Ronald Vernimmen

Contents (29-31 March 2011) Software Training 29

31 March 2011 (slides 4-54)

1. Explorer.xml a. adding extra extents b. adding additional GIS layers (SHP) c. adding additional GIS layers (raster) 2. Adding Locations 3. Editing data within DEWMS 4. Exporting data from DEWMS database

Contents (11-13 May 2011) Fews documentation: http://public.wldelft.nl/display/FEWSDOC/Home Training 11

13 May 2011 (slides 55-xxxx)

1. Data transformation: day to month (time series) (incl. Exercise) 2. Filters (Data Viewer) 3. Data transformation: hour to day (time series) (incl. Exercise) 4. Data transformation: correction of TRMM data 5. Data transformation: areal averages (using polygons) 6. Data transformation: grid transformations 7. Griddisplay (Spatial Viewer) 8. Data transformation: interpolation techniques (Thiessen) 9. PCRaster transformation: Oldeman drought maps

Software GET THE SOFTWARE AND OTHER TRAINING MATERIAL HERE: ftp://124.81.160.112 user ID: share pwd: bmkg2303 ###################################################### Installers included: MapWindow (free GIS software) CSV2SHP (needs MapWindow installed) Notepad ++ (texteditor, to modify XML files)

Adding extra visual extents

Adding extra visual extents Explorer.xml (in SystemConfigFiles directory) d:\Fews\DEWMS\Config\SystemConfigFiles\ <map> WGS 1984 <defaultExtent id="Indonesia"> 88.74998474121094 148.07273864746094 10.102596282958984 -13.193964958190918 <extraExtent id="Java"> 104.51480865478516 115.0969009399414 -5.312116622924805 -9.333152770996094

Adding extra visual extents Explorer.xml (in SystemConfigFiles directory) d:\Fews\DEWMS\Config\SystemConfigFiles\ <map>

You can get the extent data automatically via DEWMS

WGS 1984 <defaultExtent id="Indonesia"> 88.74998474121094 148.07273864746094 10.102596282958984 -13.193964958190918 <extraExtent id="Java"> 104.51480865478516 115.0969009399414 -5.312116622924805 -9.333152770996094

Adding extra visual extents Explorer.xml (in SystemConfigFiles directory) <map>

You can get the extent data automatically via DEWMS:

WGS 1984 <defaultExtent id="Indonesia"> example: Bali 88.74998474121094 148.07273864746094 10.102596282958984 -13.193964958190918 <extraExtent id="Java"> 104.51480865478516 115.0969009399414 -5.312116622924805 -9.333152770996094



WGS 1984 <defaultExtent id="Indonesia"> example: Bali 88.74998474121094 then press within DEWMS: 148.07273864746094 F12 you will see many options 10.102596282958984 go to R clipboard copy -13.193964958190918 current map extent <extraExtent id="Java"> 104.51480865478516 115.0969009399414 -5.312116622924805 -9.333152770996094



WGS 1984 <defaultExtent id="Indonesia"> example: Bali 88.74998474121094 then press within DEWMS: 148.07273864746094 F12 you will see many options 10.102596282958984 go to R clipboard copy -13.193964958190918 current map extent <extraExtent id="Java"> 104.51480865478516 115.0969009399414 -5.312116622924805 -9.333152770996094

Adding extra visual extents

once the new extent is included in the Explorer.xml file save it and return to the DEWMS. Then go to File (top left) and select Reload configuration (F5) The software will reload all and will show the new extent

Adding extra visual extents if a mistake was made DEWMS will return an error message. This message is displayed in the log screen. Inspect the message to fix the problem

Adding extra visual extents error message, just close the tab

if you want the log messages to appear again click on the 6. Logs tab

Adding extra GIS layers (SHP)

Adding extra GIS layers (SHP) Explorer.xml (in SystemConfigFiles directory) d:\Fews\DEWMS\Config\SystemConfigFiles\ <esriShapeLayer id="Rivers_Java"> Main_River_LL_polyline WGS 1984 false %NAMA% blue <esriShapeLayer id="TRMM cells"> trmmfinalpngno-ll-rv110320 WGS 1984 false %GRIDCODE% black white 70

Adding extra GIS layers (SHP) Explorer.xml (in SystemConfigFiles directory) d:\Fews\DEWMS\Config\SystemConfigFiles\

This name will appear in the layer box of the DEWMS

<esriShapeLayer id="Rivers_Java"> Main_River_LL_polyline WGS 1984 false %NAMA% blue <esriShapeLayer id="TRMM cells"> trmmfinalpngno-ll-rv110320 WGS 1984 false %GRIDCODE% black white 70


All GIS shape files are located in the MapLayerFiles directory:

<esriShapeLayer id="Rivers_Java"> Main_River_LL_polyline WGS 1984 d:\Fews\DEWMS\Config\MapLayerFiles\ false %NAMA% All new SHP files should be blue copied to this directory! <esriShapeLayer id="TRMM cells"> trmmfinalpngno-ll-rv110320 WGS 1984 false %GRIDCODE% black white 70


Make sure you use the correct projection!



Do you want the layer to be visible on start up?



What information from the shape file (attribute) do you want to see when you select the polygon within DEWMS?



What information from the shape file (attribute) do you want to see when you select the polygon within DEWMS?



Multiple Attributes can be included as well!

<esriShapeLayer id="Rivers_Java"> Main_River_LL_polyline WGS 1984 for instance: false River: %NAMA% River: %NAMA% Province: %PROVINCE% Province: %PROVINSI% blue <esriShapeLayer id="TRMM cells"> trmmfinalpngno-ll-rv110320 WGS 1984 false %GRIDCODE% black white 70


You can see the result of the changes in the DEWMS. To show the information you need to press the info (tooltip) button first and then move your mouse to the polygon / polyline of interest

Adding extra GIS layers (SHP) Explorer.xml (in SystemConfigFiles directory) d:\Fews\DEWMS\Config\SystemConfigFiles\ <esriShapeLayer id="Rivers_Java"> Main_River_LL_polyline WGS 1984 false River: %NAMA% Province: %PROVINSI% blue <esriShapeLayer id="TRMM cells"> trmmfinalpngno-ll-rv110320 WGS 1984 false %GRIDCODE% black white 70


you can set the line and fill colours

<esriShapeLayer id="Rivers_Java"> Main_River_LL_polyline WGS 1984 false %NAMA% blue you can use HTML code for a specific colour. <esriShapeLayer id="TRMM cells"> trmmfinalpngno-ll-rv110320 WGS 1984 You can find the HTML code via the following website: false %GRIDCODE% http://javaboutique.internet.com/ColorPicker/ black white 70


you can set the line and fill colours

you can use HTML code for a specific colour. You can find the HTML code via the following website: http://javaboutique.internet.com/ColorPicker/


you can set the line and fill colours, however using html code <esriShapeLayer id="Rivers_Java"> Main_River_LL_polylineyou need to change WGS 1984 line with 0F0F0F false similarly for the line %NAMA% blue <esriShapeLayer id="TRMM cells"> trmmfinalpngno-ll-rv110320 WGS 1984 false %GRIDCODE% black 0F0F0F 70


you can make the layer transparent (0 100%)


Adding extra GIS layers (raster) Explorer.xml (in SystemConfigFiles directory) d:\Fews\DEWMS\Config\SystemConfigFiles\ <esriShapeLayer id="Rivers_Java"> Main_River_LL_polyline WGS 1984 false %NAMA% blue <esriAsciiGridLayer id="Land Cover"> globcover_javabali.asc false

you can also add raster data, only ArcInfoAsciiGrid, no GeoTiff possible yet

html colour codes

Adding extra GIS layers (raster) Explorer.xml (in SystemConfigFiles directory) You can download the d:\Fews\DEWMS\Config\SystemConfigFiles\

GlobCover V2.2 data here: http://ionia1.esrin.esa.int/index.asp

<esriShapeLayer id="Rivers_Java"> Main_River_LL_polyline WGS 1984 false %NAMA% blue <esriAsciiGridLayer id="Land Cover"> globcover_javabali.asc false

Reference: ESA: GlobCover Land Cover v2.2, European Space Agency GlobCover Project, led by MEDIAS-France, 2008

html colour codes

Adding extra GIS layers (raster)

The SRTM 90 m elevation data can be downloaded here: http://srtm.csi.cgiar.org Reference: Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled seamless SRTM data V4, International Centre for Tropical Agriculture (CIAT), 2008

Adding new locations


All location information is stored in GIS shape file stored in MapLayerFiles directory: d:\Fews\DEWMS\Config\MapLayerFiles\ MetadataHydroMeteoStationsIndonesia-LL-RV110329.dbf


method 1

You can open this dbf file in Excel / Open Office / ArcGIS MetadataHydroMeteoStationsIndonesia-LL-RV110329.dbf


method 1

Method 1: Add new location info (LocationId / LocationName / coordinates / Type) on last line and save. MetadataHydroMeteoStationsIndonesia-LL-RV110329.dbf

You can edit this dbf file in Open Office (Excel 2007?) MetadataHydroMeteoStationsIndonesia-LL-RV110329.dbf


method 2

Method 2: Add new location info (LocationId / LocationName / coordinates / Type) on last line and save as CSV file. Using software CSV2SHP.exe you can create GIS shape layer from CSV file. Within this CSV file coordinates should be included!


method 2

NOTE: The following columns need to be filled in the CSV file otherwise the system will not show the new locations! LOCATIONID LOCATIONNA LAT LON TYPE and PROJECT If you know the Timestep for which you have data add this to the correct columns as well! (columns DAILY HOUR MIN5 MIN15 MONTH) In DIALY column write Daily if you have daily data HOUR write hour, MIN5 write 5-min, MIN15 write 15-min, MONTH write Month (this is CASE SENSITIVE so make sure you write Month instead of for instance MONTH The remaining columns can be left empty (using: - )


method 2

Method 2: Add new location info (LocationId / LocationName / coordinates / Type) on last line and save as CSV file. Using software CSV2SHP.exe you can create GIS shape layer from CSV file. Within this CSV file coordinates should be included! NOTE: it appears that CSV2SHP.exe is not working on Windows Vista. Is a separate installer available? UPDATE May 2011 >> try the version with DotNET installed http://www.mapwindow.org/downloads/index.php?show_details=30 First install MapWindowx86Full-v48RC1-installer.exe, after which install add on CSV2SHP.exe (uses MapWindow library files)


method 2

1. Start up CSV2SHP.exe


method 2

NOTE: make sure the file you select is not in use by another program otherwise error


method 2

3. Open the file by clicking the Open File button


method 2

4. Select the X-Field and Y-Field from the Attributes in the CSV files. These data are needed to plot the locations on the correct position


method 2

5. Select the box: Add coordinates to Shapefile name to the output file

Editing data within DEWMS 1. first select a location and parameter which you like to edit (add new data for example or correct an earlier made typing error)

Editing data within DEWMS

2. press the Data Editor button

Editing data within DEWMS 3. go to the table and click on the column with the correct date for which you have data and start entering the data. It is also possible to copy-paste a whole time series from Excel You will see the data automatically appearing in the graph to the right of the table NOTE: if you copypaste values from Excel and you have missing values make sure this missing value is denoted by NaN (Not A Number) instead of -999! otherwise the -999 is stored as a value in the database

Editing data within DEWMS 4. once finished and you want to store the data in the database click the Apply button. You will be asked if you really want to save the changes to the database

Editing data within DEWMS Note: if the table has no empty cells anymore and you have more recent data, you can extent the table by zooming out on the graph (using scroll button) until the period for which you have data appears on the axis!

Exporting data from DEWMS database 1. There are 2 ways of exporting data from the DEWMS database to for instance csv format for easy data sharing or archiving purposes. Method 1: Select a location and parameter you want to export data and click the Plots button

Exporting data from DEWMS database A graph will appear with the data for the selected station. 2. Click the Table (F7) button

The data table will appear

Exporting data from DEWMS database

3. Select the data in the table (Select All) you would like to export and Save As (both options available under the right mouse button)

Exporting data from DEWMS database 4. change the file format from PI XML files to CSV files (or any other save

Exporting data from DEWMS database 1. There are 2 ways of exporting data from the DEWMS database to for instance csv format for easy data sharing or archiving purposes. Method 2: Select a location and parameter you want to export data and via File select Export Timeseries

Exporting data from DEWMS database Using this method you can select the period for which you would like to export the data. Select both the Module Instance / Status and Reliability boxes and click OK and save

Data transformations For example: day to month (a so-called statistic summary transformation)

see file AggregateToMonth 1.00 default.xml in directory d:\Fews\DEWMS\Config\ModuleConfigFiles\

Data transformations (day to month) <moduleInstanceId>CSV_Import scalar input timestep <parameterId>P.m PrecipitationStations external historical read complete forecast other periods can be defined as well, for instance: <statistics> year <season endMonthDay="--01-31" startMonthDay="--01-01"/> however, then you need to change the timeStep <season endMonthDay="--02-29" startMonthDay="--02-01"/> unit from <season endMonthDay="--03-31" startMonthDay="--03-01"/> <season endMonthDay="--04-30" startMonthDay="--04-01"/> <season endMonthDay="--05-31" startMonthDay="--05-01"/> nonequidistant >>> exercise! <season endMonthDay="--06-30" startMonthDay="--06-01"/> <season endMonthDay="--07-31" startMonthDay="--07-01"/> <season endMonthDay="--08-31" startMonthDay="--08-01"/> <season endMonthDay="--09-30" startMonthDay="--09-01"/> <season endMonthDay="--10-31" startMonthDay="--10-01"/> other available <season endMonthDay="--11-30" startMonthDay="--11-01"/> functions for <season endMonthDay="--12-31" startMonthDay="--12-01"/> instance: minimum (min) maximum (max) (see next slide <moduleInstanceId>AggregateToMonth for all options) scalar <parameterId>P.m PrecipitationStations external historical output timestep add originals

Data transformations other Statistics Summary Transformations available:

http://public.wldelft.nl/display/FEWSDOC/02+Transformation+Module Delft-OMS documentation\1. 02 Transformation Module.pdf max min sum count mean median standardDeviation percentileExceedence percentileNonExceedence quartile skewness kurtosis variance rsquared rootMeanSquareError

Exercise: data transformation (day to year) <moduleInstanceId>CSV_Import scalar <parameterId>P.m PrecipitationStations this id will return in the external historical log messages read complete forecast <statistics> <season endMonthDay="--12-31" startMonthDay="--01-01"/> <moduleInstanceId>AggregateToYear scalar <parameterId>P.m PrecipitationStations external historical

the transformation will be calculated for all data available in the database

add originals

EXERCISE: Create a new ModuleConfigFile the locations within the LocationSetId PrecipitationStations

Exercise: data transformation (day to year) Tip: create a copy of the AggregateToMonth 1.00 default.xml as starting point and rename to AggregateToYear 1.00 default.xml Once you have created the AggregateToYear 1.00 default.xml and before it is recognised by the software you need to register this new file in the ModuleInstanceDescriptors.xml (in the RegionConfigFiles directory) Tip: Look for AggregateToMonth in the ModuleInstanceDescriptors.xml and copy-paste and replace the AggregateToMonth with AggregateToYear Now that the new file is registered it can be incorporated in the workflow. Since at the moment we only do the transformation on scalar data (timeseries at one or more locations) we incorporate it in the CSV_Import.xml (see the WorkflowFiles directory)

* CSV_Import * AggregateToMonth add AggregateToYear and save the file.

Note: AggregateToDay is also included now

Note: In this case from CSV_Import 1.02 default.xml to CSV_Import 1.03 default.xml This way we can track the changes more easily. Even better: in addition keep a logbook! >>> logbook changes configuration.txt

Exercise: data transformation (day to year) Note the difference in notation of timestep day, month and year!

Exercise: data transformation (day to year) Now that we have created and updated all the files we can reload the configuration from within the DEWMS. By pressing F5 or via File Reload Configuration.

Once the new configuration files have been loaded and no error messages appear in the log message box we can run the workflow: Import measurements (CSV format) This workflow will first import the measurements in CSV format (the ModuleConfigFile: CSV_Import), next it will aggregate the measurement data to monthly timesteps (AggregateToMonth) followed by annual timesteps (AggregateToYear)

Exercise: data transformation (day to year) Tip: if you only want to run the AggregateToYear ModuleInstance you can also untick the other ModuleInstances. (1) First select the workflow you want to run (2) Press CTRL-R or first F12 and then select

(3) Select the modules you want to include and press OK and run the workflow.

Note: AggregateToDay is also included now

Filters (Data Viewer) Now we want to visualize the annual data which was just created with the AggregateToYear ModuleInstance. For this we need to adjust the Filters.xml (in the RegionConfigFiles directory) the names can be seen in the Data Viewer

The file currently looks as follows:

Note: slight changes in Filter names: Precipitation (month) (transformation) and additional filter Precipitation (daily) (transformation)

Filters (Data Viewer) (1) add a new filter id: <mapExtentId>Indonesia

Note: slight changes in Filter names: Precipitation (month) (transformation) and additional filter Precipitation (daily) (transformation)

(2) find in the filters.xml the text: Precipitation_month_Indonesia: <moduleInstanceId>AggregateToMonth scalar <parameterId>P.m PrecipitationStations external historical editing visible to all future task runs <synchLevel>5

Filters (Data Viewer) (3) copy-paste the Precipitation_month_Indonesia section and change the relevant lines (indicated in blue): Note: slight changes in

Filter names: Precipitation (month) <moduleInstanceId>AggregateToMonth (transformation) and scalar <parameterId>P.m additional filter PrecipitationStations Precipitation (daily) external historical (transformation) editing visible to all future task runs <synchLevel>5

(4) save the file once changed and reload the configuration within the DEWMS (F5). Check if the annual data is now visible: ignore the error you may get:

Filters (Data Viewer) For instance Location Situraja (BMKG) should look like the following:

Other data transformations Available data transformations listed here: http://public.wldelft.nl/display/FEWSDOC/02+Transformation+Module

Delft-OMS documentation\1. 02 Transformation Module.pdf and here: http://public.wldelft.nl/display/FEWSDOC/20+Transformation+Module+(Improved+schema)

Delft-OMS documentation\2. 20 Transformation Module (Improved schema).pdf

Focussing on data aggregation transformations for the moment: http://public.wldelft.nl/display/FEWSDOC/Aggregation+transformations

Delft-OMS documentation\3. Aggregation transformations.pdf

And as example hour to day: 1. accumulative (precipitation hour to day) See documentation: http://public.wldelft.nl/display/FEWSDOC/Aggregation+Accumulative

Delft-OMS documentation\4. Aggregation Accumulative.pdf

Data transformation (hour to day; accum.) see file AggregateToDay 1.00 default.xml in directory d:\Fews\DEWMS\Config\ModuleConfigFiles\ <moduleInstanceId>CSV_Import scalar <parameterId>P.m PrecipitationStations external historical read complete forecast <moduleInstanceId>AggregateToDay scalar <parameterId>P.m PrecipitationStations external historical add originals

Note: currently needs relativeViewPeriod, issue has been reported

Data transformation (hour to day; accum.) ModuleInstanceDescriptors (in the RegionConfigFiles directory)

<moduleInstanceDescriptor id="AggregateToDay"> <description>Aggregate timeseries to daily timestep <moduleId>TransformationModule

<moduleInstanceDescriptor id="AggregateToMonth"> <description>Aggregate timeseries to monthly timestep <moduleId>Transformation

Data transformation (hour to day; accum.) Import the hourly timeseries set which is provided for Bandung and run the

Note: You only need to run the CSV_Import and AggregateToDay ModuleInstances Note: Make sure your system time is set to 01-01-2012 prior to running the workflow 1. Check via the Data Viewer if the hourly data for StationName Bandung Stasiun Geofysika are imported (see the Precipitation(hour) filter) 2. Check via the Data Viewer if the daily data for StationName Bandung Stasiun Geofysika have been generated (see the Precipitation (daily) (transformation) filter) 3. Plot both hour and daily data next to each other for the Bandung Stasiun Geofysika data and inspect the table

Data transformation (hour to day; accum.) 3. Plot both hour and daily data next to each other for the Bandung Stasiun Geofysika data and inspect the table

daily data stored in database with ModuleInstanceId: AggregateToDay hourly data stored in database with ModuleInstanceId CSV_Import

3.4 + 0.1 + 0.8 + 21.4 + 2.4 + 0.3 + 1.0 = 29.4 mm

Exercise: data transformation (hr to day; mean) we use the aggregation InstantaneousToMean see: http://public.wldelft.nl/display/FEWSDOC/Aggregation+InstantaneousToMean

Delft-OMS documentation\5. Aggregation InstantaneousToMean.pdf

Implement this function in the AggregateToDay 1.00 default.xml file following the example in the documentation Some tips:

2. Check if the correct parameters and locationSets are identified (inspect the CSV_Import ModuleConfigFile for this to make sure) 3. Allow for missing values

Note: Note: workflowfile Note:

Exercise: data transformation (hr to day; mean) 1. Check via the Data Viewer if the Daily Waterlevel for StationName S. Cisadane Batubeulah are visible. 2. Compare both the hourly imported water level data with the daily mean water level data for the S. Cisadane Batubeulah station

Exercise: data transformation (hr to day; mean) 2. Compare both the hourly imported water level data with the daily mean water level data for the S. Cisadane Batubeulah station

average 22-09-2010 01:00 until 23-09-2010 00:00 = 1.90 m

Data transformation (correction of TRMM) The monthly TRMM data are corrected using the equation derived during the ground station validation using a sosee: http://public.wldelft.nl/display/FEWSDOC/UserSimple+Transformation

Delft-OMS documentation\6. UserSimple Transformation.pdf 1. input and output variables are defined (see file 3B42RT_CorrectionMonth 1.05 default.xml in ModuleConfigFiles directory input: TRMM_Indo_month_land <moduleInstanceId>CalculateMonthlyPrecipitationAtGridLand grid <parameterId>P.obs.trmm.3b42rt Indonesia external historical add originals


Delft-OMS documentation\6. UserSimple Transformation.pdf

output: TRMM_Indo_month_corrected_land <moduleInstanceId>3B42RT_CorrectionMonth grid <parameterId>P.obs.trmm.3b42rt.corrected Indonesia external historical add originals

Note: the other variableId, moduleInstanceId and parameterId compared to the input variable!


Delft-OMS documentation\6. UserSimple Transformation.pdf

<user> <simple> <expression>3.199421875 * TRMM_Indo_month_land^0.791096244 TRMM_Indo_month_corrected_land

Note:

in a later stage we will use the coefficientSetFunctions as shown in the documentation. For instance to convert discharge from m3/s to mm using the catchment area as a coefficient id which is unique for each location

ANALISA KEKERINGAN DENGAN BERBAGAI PENDEKATAN

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APAKAH KEKERINGAN ITU ? Kurangnya hujan yang turun dari biasanya. Dampaknya : - soil moisture berkurang tanaman mati - tampungan air permukaan berkurang air waduk; sungai menyusut sawah teknis puso, kesulitan air minum, listrik mati - tampungan air tanah berkurang Akibat : dampak sosial, ekonomi, lingkungan 6/14/2013

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Sifat Kekeringan : Kejadiannya Lamban Dipengaruhi oleh Hujan: Besar Defisit Intensitas Kekeringan Durasi Defisit 6/14/2013

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Metode Perhitungan Indeks Kekeringan Prosentasi terhadap Hujan Normal Desil Standardized Precipitation Index (SPI) Teori of Run

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SPI Indeks Kekeringan untuk Monitoring

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Indeks Kekeringan yang Digunakan STANDARDIZED PRECIPITATION INDEX atau SPI Dikembangkan oleh Mc Kee tahun 1993 Indeks sederhana, dihitung dari hujan bulanan, merupakan probabilitas hujan yang sudah ditransformasikan ke indeks Digunakan di lebih dari 40 negara Skala waktu bervariasi, mencerminkan berbagai gambaran temporal yang dapat digunakan untuk mengevaluasi hujan dan kondisi suplai (tanah, sungai, danau, waduk)

Prosedur Perhitungan SPI pdf 1,0

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rata-rata (µ) mendekati nol, simpangan baku ( ) sekitar 1

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-2

-3 93

94

95

96

97

98

99

00

01

02

Tahun SPI-1

Ringan (mild) 6/14/2013

SPI-6

garis 0

garis -1

garis -2

Parah (severe) Ekstrim (extreme)

10

KLASIFIKASI SPI : Ukup

KLASIFIKASI

2.00

Amat sangat basah

1.50 1.99

Sangat basah

1.00 - 1.49

Cukup basah

-0.99 - 0.99

Mendekati normal

-1.00 - (-1.49)

Cukup kering

-1.50 (-1.99) -2.00 atau < (-2.00)

Sangat kering Amat sangat kering

Gambar D-6. Peta Sebaran SPI-1 tahun 1994, bulan April SPI-1 SWS -0,2; Mei -2,4; Juni -2,1; Juli 2,6; Aug -1,8; Sep -2,2; Okt 0,6; Nov -0,03 6/14/2013

12

Gambar D-7. Peta Sebaran SPI-1 tahun 1997, bulan April SPI-1 SWS +0,6; Mei -0,3; Juni -0.7; Juli -1,4; Aug -1,6; Sep -2,4; Okt 2,2; Nov -1,4; Des -0,3

6/14/2013

13

Theory of Run Indeks Kekeringan untuk perencanaan bangunan air

6/14/2013

14

6/14/2013

15

Durasi dan Sum Defisit Pos Bojong (23) Pekalongan

800 700 600 500 400 300 200 100 0

Hujan Bulanan Rata2 hujan Bulanan Rata2(1916-1982) Sum Defisit

Durasi

1973 Jan

Apr

1974 Jul

Oct

Jan

Apr

1975 Jul

Oct

Jan

Apr

Jul

Oct

Waktu (bulan) 6/14/2013

16

6/14/2013

17

Drought mapping and implementation in DEWMS

Ronald Vernimmen

Contents Oldeman (agroclimatic maps for Indonesia) Standardized Precipitation Index (SPI) (Palmer Drought Severity Index)

Oldeman agroclimatic maps

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 Oldeman documentation\An Agroclimatic Map of Kalimantan, Maluku, Irian Jaya & Bali, West & East Nusa Tenggara.pdf Oldeman documentation\An Agroclimatic Map of Sumatra.pdf

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.

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.

Classification Results in a total of 18 climatic zones (14 of which in Indonesia):

A B1 B2 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3 E4

WET months >9 7-9 7-9 5-6 5-6 5-6 3-4 3-4 3-4 3-4 <3 <3 <3 <3

DRY months <2 <2 2-3 <2 2-3 4-6 <2 2-3 4-6 >6 <2 2-3 4-6 >6

Map Kalimantan

Implementation in DEWMS Oldeman classification based on corrected monthly TRMM satellite precipitation. Steps within DEWMS: 1. import TRMM 3B42RT data 2. fill gaps and aggregate 3 hr to month 3. correct the monthly data based on groundstation validation relation 4. calculate average monthly precipitation (2002-now) for each month, so average for January, February and so on 5. Determine for each grid cell if on average grid cell is dry or wet 6. Calculate the Oldeman class based on the number of dry and wet months (at the moment not yet consecutive!) using PCRaster incorporated in Delft-OMS. Also available as stand alone program (free, see www.pcraster.geo.uu.nl)

Map Kalimantan

Map Indonesia

Drought indices many different drought indices: see http://www.drought.unl.edu/whatis/indices.htm Drought documentation\Drought Indices.pdf One of the simplest (only needs monthly precipitation) is SPI.

Standardized Precipitation Index (SPI) from http://www.ncdc.noaa.gov/paleo/drought/drght_spi.html SPI documentation\McKee et al. 1993.pdf 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. Because droughts vary greatly in duration, it is important to detect and monitor them at a variety of time scales. Short-term droughts are measured by meteorological instruments and are defined according to specific regional climatology. Agriculturally important droughts result in deficits in soil moisture and three- to six-month droughts can have a great impact. Longer-term droughts (months to years) can have important impacts on surface and ground water supplies.

Standardized Precipitation Index (SPI) from http://www.ncdc.noaa.gov/paleo/drought/drght_spi.html Values of SPI are derived by comparing the total cumulative precipitation for a particular station or region over a specific time interval (for example: the last month, the last 3 months, the last 6 months) with the average cumulative precipitation for that same time interval over the entire length of the record. For example, total precipitation in May of any given year for the northwestern Kansas climate region would be compared to average total precipitation for that region for all Mays in the record, 1895-1998. 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.

Standardized Precipitation Index (SPI) from http://www.ncdc.noaa.gov/paleo/drought/drght_spi.html The classification values for SPI values are: 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

A drought event is defined when the SPI is continuously negative and reaches a value of -1.0 or less, and continues until the SPI becomes positive. Drought duration is defined by the interval between the beginning and end of that period. The magnitude of the drought event is measured by the sum of the SPI values for the months of the drought.

SPI data requirements monthly precipitation series for station or region for at least 30 years, preferred is 60 years the method is described here: http://climate.atmos.colostate.edu/standardizedprecipitation.shtml SPI documentation\spi.pdf

Questions: How does PusAir and BMKG calculate SPI? Special software? PusAir uses Excel / BMKG uses special software incorporating data from 178 synoptic stations for the whole of Indonesia SPI calculated for each individual station or for a region? >> for each of the 15 stations within the Pemali-Comal basin. How is it scaled to the region? PusAir uses Kriging interpolation (within Surfer software)

Example of calculation using Excel example described here: SPI documentation\Giddings et al. 2005.pdf

and following Appendix A of the publication: SPI documentation\Giddings et al. 2005 example - RV110510.xls NOTE: equations in the publication of Giddings et al. 2005 are wrong!!! Use the equations in the spi.pdf document

1. start with 43 year timeseries of monthly precipitation in January (Column B)

Example of calculation 2. first we take a look at the distribution of these values for more info on normal distribution and equations: https://secure.wikimedia.org/wikipedia/en/wiki/Normal_distribution SPI documentation\Normal distribution.pdf average +/- 1 x standard deviation

average +/- 2 x standard deviation

Example of calculation 3. the values are not normally distributed (positively skewed) and need to be transformed to normal distribution using gamma distribution 4. the monthly precipitation data are log normally transformed ln(x) for each value (column C)

Example of calculation 5a. the log normally transformed monthly precipitation values are then transformed by the gamma distribution (column E)

Equations here taken from the spi.pdf

Example of calculation 5b. alpha and beta can be calculated

In the example the constant A of the equations given here is the constant U! see Column D

Example of calculation 6. once the gamma function is applied another statistical trick: t-transform (NEEDS MORE EXPLANATION!) (Column F)

Example of calculation 7. using some constants and the transformed t SPI can be calculated (Column G)

Questions We have now calculated SPI-1 month for January Mexico. We can do this for the other months as well.

But how do we calculate SPI-3. Do we take the sum of the 3 months together??? >> YES!

Possibilities for implementation in DEWMS how to implement the gamma distribution function? using R?

Henny Maria

1 Perdas 2 Hymos 3TIDEDA 4 Neo Perdas 5 HITA

------

DPMA (1980) Belanda New Zeland Puslitbang Air (2001) Puslitbang Air (2007)

Perdas, Mengolah data debit dari data mentah (dapat mendigit grafik AWLR) menjadi data debit harian menggunakan koreksi panggerusan dan pengendapan, buatan Indonesia sehingga sudah disesuaikan dengan iklim di Indonesia, Gratis Hymos, mengolah data hidrologi baik debit hujan dan klimatologi, buatan belanda, tidak ada koreksi penggerusan dan penegndapan harus beli dapat untuk analisa hidrologi TIDEDA, mengolah data hidrologi baikhujan debit maupun klimatologi, dapat mendigit grafik AWLR ARR, maupun data Klimatologi lainnya , tidak ada koreksi penggerusan dan pengendapan, lebih kearah database( harus beli) Neo Perdas, hampir sama seperti Perdas tapi sudah disesuaikan dengan window yang sekarang ada (gratis) HITA, untuk mendigit grafik AWLR maupun ARR, tidak perlu digitizer, data bisa di scan dan didigit di monitor, ( Gratis)

OLEH Henny Maria

Sejarah Perangkat Lunak Pengolahan Data Hidrologi 1980

1990

PERDAS for DOS dibuat oleh Balai Hidrologi.

Masih menggunakan bahasa Fortran.

PERDAS for DOS direvisi dan diganti menjadi

menggunakan bahasa Basic.

2000

NEO PERDAS.

Merupakan hasil revisi dari PERDAS for DOS yang bisa dioperasikan dibawah sistem operasi for Windows

Sejarah Perangkat Lunak Pengolahan Data Hidrologi 2007

HITA 1

Perangkat lunak yang mampu mendigit data grafik baik grafik hujan, tinggi muka air maupun klimatologi tanpa menggunakan digitizer, tetapi cukup discan di layar monitor.

2009

HITA 1.1

Penyempurnaan dari HITA 1. selain mendigit grafik biasa, juga mampu mendigit grafik hujan yang berbentuk lengkung.

Ruang lingkup Ruang lingkup dalam analisa data debit sungai terdiri dari: 1) analisa lengkung debit, 2) analisa data tinggi muka air dan 3) analisa debit rata-rata.

3 Sub Program NEO PERDAS Hdnmstns Editor Digunakan untuk mengedit (entry) keterangan stasiun (pos)

Rating Curve

Neoperdas 2000

proses input data pengukuran untuk mendapatkan lengkung aliran dan parameter a, b, dan c

HDM2 HDM21 HDM1 HDM11 SFPEAKS HDM22 PUBLEV PUBDEB GRFLOW3

Pembuatan Rating Curve Data untuk pembuatan lengkung debit sungai/saluran terbuka, perlu memperhatikan hal sebagai berikut : Tinggi muka air pengukuran harus sama atau mendekati sama dengan tinggi muka air pengamatan di pos duga air. Harus ada data tinggi muka air tertinggi dan terendah yang pernah terjadi selama pengamatan. Harus ada informasi tinggi muka air pada debit nol. Harus ada gambar penampang melintang. Harus ada informasi penggerusan dan pengendapan dasar sungai/saluran terbuka di lokasi pengukuran debit.

Contoh gambar penampang melintang sungai yang harus ada untuk pembuatan lengkung debit

CONTOH LENGKUNG DEBIT YANG DI BUAT SECARA MANUAL

PEMBUATAN LENGKUNG DEBIT DENGAN METODE AREA VELOCITY

Pengukuran aliran sungai :

No.

12 13 14 15 16 17 18 19 20

Bt. Sumani Bandar Padung

Tanggal. 1981 28-Aug 26-Oct 1982 14-Apr 15-Sep 21-Oct 1983 26-Jan 29-Sep 28-Nov 1984 16-Aug

Disalin Oleh

Diukur oleh

Lebar (m)

Luas (m2)

Vm (m/det)

M.A (m)

Q (m3/det)

L.A

Nomor SL

:

1-66-0-05

Tahun

:

__________

Koreksi (m)

Perc. (%)

Metode

Jumlah vertikal

Perubahan M.A.

Waktu mulai (jam)

selesai (jam)

Oma Warma + Yoyo S Oma Warma + Yoyo S

26.5 28.5

42.7 87.0

0.22 0.81

1.30 2.77

9.44 70.2

0 0

-4.89 -0.14

02.08 02.08

23 22

-0.10

13.13 11.30

14.05 15.25

Oma Warma + Yoyo S Azizar + Basyir Azizar + Basyir

29.5 31.5 25.0

72.1 19.5 42.2

0.74 0.22 0.18

2.49 1.06 1.24

53.2 4.33 7.73

0 0 0

+0.36 +0.69 -0.65

02.08 02.06.08 02.08

21 23 21

-0.08 -

17.00 10.30 11.45

18.00 11.30 12.50

Adis + Supena Azizar + Basyir Azizar + Basyir

36.0 25.0 26.0

175.0 34.0 41.0

1.50 0.71 0.28

5.50 1.19 1.39

280. 5.95 11.5

0 -0.04 0

-1.08 +10.1 -2.68

06 02.06 02.06

20 23 23

+0.01

10.45 9.50 11.24

11.35 10.36 12.23

Oma Warma + Adis

23.0

31.8

0.11

1.03

3.62

0

+6.70

02.06

21

-

9.20

10.13

: Oma Warma

Tanda Tangan :

Keterangan

Diperiksa Oleh

: Dra. Sri Mulat Y

Penanggung jawab : Drs. Soewarno

Tanda Tangan

:

Tanda Tangan

:

Rating Curve. yaitu proses input data pengukuran untuk mendapatkan lengkung aliran dan parameter a, b, dan c Tampilan dari sub program rating curve

1 2 4 3

1. 2. 3. 4. 5. 6. 7.

6

7

5

Icon .. klik untuk membuka keterangan stasiun (HDMSTNS) dan direktori yang diinginkan. Pemilihan jenis file, menggunakan Microsoft Exel atau menggunakan file dbf. Icon untuk merubah dari file Exel ke dbf atau sebaliknya. Pemilihan untuk mengakses file dbf, bila file ada sedikit kerusakan Icon ? berisi keterangan penggunaan point 4 Icon untuk menganalisa input dan proses selanjutnya Nama file data

1. Tunjuk direktori yang dituju dan klik file keterangan stasiun

Double klik ratcuvData.xls untuk pengisian data pengukuran debit, apabila dalam direktori yang dimaksud belum ada datanya form tersebut masih kosong dan bila sudah ada data maka form akan terlihat datanya dengan penampilan sebagai berikut :

Urutan entry data sebagai berikut

:

Isi No. urut Isi kolom tanggal dengan tanggal, bulan, dan tahun sesuai dengan regional seting Isi kolom H dengan tinggi muka air hasil pengukuran Isi kolom Q dengan debit dalam m3/det Kolom nama stasiun bisa diabaikan Klik icon save Kembali pada panel Analisa Rating Curve dengan mengklik Analisa Rating Curve pada bar bagian bawah dan tampilan sebagai berikut.

Entry data pengukuran debit untuk pembuatan tabel debit dan perhitungan koreksi tinggi muka air menggunakan FormH2.xls seperti berikut ini :

5. Klik Analisa Input untuk proses selanjutnya dan muncul panel sebagai beikut :

a. Klik Yes apabila setuju (realstat diambil dari file HDMSTNS) b. dan klik No bila tidak setuju dengan nama realstat tersebut atau bila nama realstat lebih dari satu dan muncul panel berikut

Pada bar diisi nama baru atau dikosongkan Klik cancel bila tidak jadi Klik OK bila melanjutkan proses Tampilan selanjutnya adalah panel pemilihan nama realstat

Keterangan : Ploting data pengukuran Icon transfer data pengukuran menjadi file format H2 (resume) Icon buka file hasil analisa Icon copy grafik lengkung hasil analisa ke program Exel untuk diedit dan di print Icon buat file tabel hasil lengkung untuk setiap perbedaan tinggi muka air 10 cm. Panel regresi untuk analisa data menggunakan Ratcuv ataupun Lograt Parameter a, b, dan c hasil analisa dengan rumus Q = c(H ± a)b Klik panel regresi untuk proses selanjutnya, pilih metoda regresi yang digunakan Ratcuv atau Lograt

Tentukan tahun data yang diproses dari tahun sampai tahun berapa.

Klik Icon Terus untuk proses pembuatan lengkung seperti gambar di bawah ini

Bila menggunakan metoda regresi Lograt nilai c nya bisa ditetapkan serta b bisa ditentukan antara interval berapa sampai berapa. Rumus Lograt adalah Q = a(H ± c)b sedangkan di panel grafik tertulis menggunakn rumus Ratcuv Q = c(H ± a)b, jadi parameter c di Lograt = parameter a di Ratcuv, dengan tahapan sebagai berikut :

Klik Panel regresi dan klik regresi menggunakan lograt exe seperti di bawah ini

Panel selanjutnya adalah grafik regresi dengan menggunakan lograt seperti di bawah ini.

Di panel tertulis nilai parameter untuk rumus RATCUV, yaitu : A = -.403 (parameter C untuk Lograt) B = 1.7561 C = 12.5572 (parameter A untuk Lograt)

Merubah parameter B dan C pada persamaan Lograt bila diperlukan Misal : B diisi antara 1 - 1.5 dan C dipertahankan tetap seperti panel berikut

Kemudian klik regresi menggunakan lograt exe, hasilnya sebagai berikut :

dan persamaan baru yang didapat seperti tertulis di pojok kiri bawah panel grafik yaitu : a = -.403 b = 1.5 c = 11.309 Jadi persamaan akhir adalah Q = 11.309(H 0.403)1.5

Untuk mencetak gambar lengkung klik copy ke Exel, selanjutnya penampilannya disempurnakan. Klik icon buat file table untuk membuat file table untuk perbedaan tinggi muka air 10 cm seperti gambar berikut

Keterangan : Tulis H minimum Tulis H maksimum Tulis tahun proses Klik icon buat untuk proses membuat file tabel seperti di bawah ini Klik icon n g g a j a d i untuk membatalkan proses

JOINT COOPERATION PROGRAMME

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