The Rijkswaterstaat historical time series data can be accessed via Waterbase. Waterbase is a mechanism that provides a web-service for a subset of the DONAR (in Dutch:Data Opslag NAtte Rijkswaterstaat) database that contains all historical data for all water-related Rijkswaterstaat affairs. MWTL is the name of the monitoring programme (in Dutch: Monitoring Waterstaatkundige Toestand des Lands Milieumeetnet rijkswateren) DONAR also contains grids, lines etc. For these non time series data see the links on Rijkswaterstaats waterdata_waterberichtgeving, this also includes Near Real Time (NRT) data in a geoviewer. NRT data feeds are available via www.rws.nl/rws/opendata. Much information on RIjkswaterstaat data is also present in the HelpDeskWater. Currently Rijkswaterstaat is working on updating the IT infrastructure to connect everything via an internal Data Distribution Layer.
http://live.waterbase.nl/wboutput.cfm? loc=KATWK &wbwns=1|Waterhoogte+in+cm+t.o.v.+normaal+amsterdams+peil+in+oppervlaktewater &byear=1737&bmonth=01&bday=01 &eyear=1808&emonth=01&eday=01 &output=Tekst &whichform=2 |
rws_waterbase_load.m and (ii) a python tool box. We recommend to choose the text files from Waterbase, and not the Excel files. The Excel files are not real (binary) Excel files, but are tab-delimited ascii files. This means that they do load well into Excel directly, but also that you cannot read them into Matlab with xlsread.
rws_waterbase_get*.m. Together with the aforementioned function to parse the ascii files (rws_waterbase_load.m), this forms a complete toolbox to work with MWTL data in Matlab.rws_waterbase*.m toolbox for Matlab. In addition, for use of the MWTL data in other analysis programs such as R and Python the rws_waterbase*.m toolbox is of no use. For both analysis languages, dedicated parse functions have to be programmed as well. Therefore, we extended the rws_waterbase*.m toolbox, so that it not only downloads the data in batch mode, but transforms the data to an internationally standardized format that can readily be used by R and python as well in addition to Matlab: netCDF-CF. This resulted in a complete workflow in Matlab that downloads Waterbase data automatically using the above mentioned web service urls, parses them into Matlab memory with rws_waterbase_load.m, and uses rws_waterbase2nc.m to save them as a netCDF-CF file , and adding preliminary Aquo meta-data. These netCDF files are made available on our open OPeNDAP server opendap.deltares.nl. We update this OpeNDAP server manually periodically. For easy querying, we harvest all meta-data, and cache that as a separate netCDF file called catalog.nc. We are planning to cache the meta-data also as a catalog.xml file, that can readily be ingested by meta-data servers such as geonetwork. As a final step we make an overview of all stations for one parameter in a kml file, aka known as Google Earth file. The Matlab script rws_waterbase_all.m performs this entire workflow. It is run periodically to update our OPeNDAP server and kml server (see Figure below).
rws_waterbase*.m toolbox