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    566 Soil Moisture Berlin-Steglitz

    DOI Info:

    • DOI:10.18728/566.0
    • Citation suggestion: Lena-Marie Kuhlemann (2021-01-18 ) Soil Moisture Berlin-Steglitz. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries. dataset. https://doi.org/10.18728/566.0
    • FRED-datapackage: 565
    Title
    Soil Moisture Berlin-Steglitz
    Period
    2019-03-20 ongoing
    Sampling interval
    1 day Irregular Interval
    Description

    Mean daily soil moisture between 0-100 cm under different urban vegetation types.

    Keywords
    Soil moisture, urban, ecohydrology
    Study site
    Steglitz Urban Ecohydrological Observatory
    Contact
    Lena-Marie Kuhlemann
    Licence for data
    All rights reserved. Please send a request to Lena-Marie Kuhlemann if you like to use this data. Mind our data policy: IGB Data Policy
    Project
    DFG RTG "Urban Water Interfaces" (UWI) Project Website
    Publications

    List of all publications related to this package:

    • Kuhlemann, L.-M., Tetzlaff, D., Smith, A., Kleinschmit, B., and Soulsby, C.: Using soil water isotopes to infer the influence of contrasting urban green space on ecohydrological partitioning, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-425, in review, 2020.

    Metadata files

    TitelUpload dateFiletypeLicenceActions
    General_MetadataSoil_Moisture_Berlin-Steglitz.xml22. Oct. 2021 03:08xmlODC-By Download
    General_MetadataSoil_Moisture_Berlin-Steglitz.eml22. Oct. 2021 03:08emlODC-By Download

    Data files (excel)

    TitelCreateFiletypeActions
    Moisture_Grassland.csv 18. Jan. 2021 09:54 datatable: .csv
    Error: To access file, please get in touch with the contact person.
    Moisture_Shrub.csv 18. Jan. 2021 09:54 datatable: .csv
    Error: To access file, please get in touch with the contact person.
    Moisture_Trees.csv 18. Jan. 2021 09:54 datatable: .csv
    Error: To access file, please get in touch with the contact person.

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    Parsing data File

    Estimated Time:

    Why does it take so much time?

    While parsing a file, the database has to perform various tasks, some of them needs a lot of CPU and memory for larger files.

    • preprocessing: means automatic detection of headlines, table body, format values or csv-separators
    • copying: means read the file cell by cell and copy all elements to the database. During this format settings can be calculated (for example iso-time)
    • analyzing: check out for different data types (can be time, numeric or text)